Sign Language Interpreter Cognitive Aptitude analysis
Freya Watkins
06 October 2021
Setup
Load packages
library(here) # file organisation & folder location
library(tidyverse) # data wrangling & plotting
library(naniar) # dealing with missing values
library(PupillometryR) # raincloud plots
library(plotrix) # for calculating standard error
library(scales) # scales on plots
library(psych) # for factor analysisR / package versions used
R.Version() ## $platform
## [1] "x86_64-w64-mingw32"
##
## $arch
## [1] "x86_64"
##
## $os
## [1] "mingw32"
##
## $system
## [1] "x86_64, mingw32"
##
## $status
## [1] ""
##
## $major
## [1] "4"
##
## $minor
## [1] "0.5"
##
## $year
## [1] "2021"
##
## $month
## [1] "03"
##
## $day
## [1] "31"
##
## $`svn rev`
## [1] "80133"
##
## $language
## [1] "R"
##
## $version.string
## [1] "R version 4.0.5 (2021-03-31)"
##
## $nickname
## [1] "Shake and Throw"packageVersion('here')## [1] '1.0.1'packageVersion('tidyverse')## [1] '1.3.1'packageVersion('psych')## [1] '2.1.6'Read in data
here::here()## [1] "C:/Users/Freya Watkins/OneDrive - University of Birmingham/Desktop/R/sli_aptitude"apt <- read_csv(here("data", "2021-09-30_SLI_aptitude_data.csv"))##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## group = col_character(),
## ppt = col_character(),
## bis_tot = col_character(),
## bis_att = col_character(),
## bis_mot = col_character(),
## bis_nplan = col_character(),
## summ = col_character(),
## copy_sign = col_character(),
## bsl_srt = col_character(),
## town_map = col_character(),
## comments = col_character()
## )
## i Use `spec()` for the full column specifications.apt <- as_tibble(apt)Look at data
head(apt)## # A tibble: 6 x 36
## session group ppt nback_lett nback_spat nback_comb corsi_bspan corsi_score
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 pilot HW001 0.68 0.76 0.72 5 35
## 2 1 pilot HW005 0.75 0.73 0.74 8 88
## 3 1 pilot HW006 0.67 0.75 0.71 6 54
## 4 1 pilot HW007 0.73 0.71 0.72 6 60
## 5 1 pilot HW008 0.75 0.73 0.74 8 80
## 6 1 pilot HW013 0.68 0.71 0.7 6 60
## # ... with 28 more variables: corsi_corr <dbl>, corsi_mspan <dbl>,
## # kirk_ceil <dbl>, kirk_raw <dbl>, kirk_acc <dbl>, kbit_ceil <dbl>,
## # kbit_raw <dbl>, kbit_acc <dbl>, dspan_mem <dbl>, dspan_corr <dbl>,
## # dspan_time <dbl>, mr2d_acc <dbl>, mr2d_rt <dbl>, mr3d_acc <dbl>,
## # mr3d_rt <dbl>, bis_tot <chr>, bis_att <chr>, bis_mot <chr>,
## # bis_nplan <chr>, mlat_raw <dbl>, mlat_acc <dbl>, summ <chr>,
## # copy_sign <chr>, bsl_srt <chr>, town_map <chr>, grade_bsl <dbl>,
## # grade_terp <dbl>, comments <chr>tail(apt)## # A tibble: 6 x 36
## session group ppt nback_lett nback_spat nback_comb corsi_bspan corsi_score
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 grant WV2051 NA NA NA NA NA
## 2 3 grant WV2061 0.78 0.76 0.77 6 48
## 3 3 grant WV2071 NA NA NA NA NA
## 4 3 grant WV2081 NA NA NA NA NA
## 5 3 grant WV2091 NA NA NA NA NA
## 6 3 grant WV2111 NA NA NA NA NA
## # ... with 28 more variables: corsi_corr <dbl>, corsi_mspan <dbl>,
## # kirk_ceil <dbl>, kirk_raw <dbl>, kirk_acc <dbl>, kbit_ceil <dbl>,
## # kbit_raw <dbl>, kbit_acc <dbl>, dspan_mem <dbl>, dspan_corr <dbl>,
## # dspan_time <dbl>, mr2d_acc <dbl>, mr2d_rt <dbl>, mr3d_acc <dbl>,
## # mr3d_rt <dbl>, bis_tot <chr>, bis_att <chr>, bis_mot <chr>,
## # bis_nplan <chr>, mlat_raw <dbl>, mlat_acc <dbl>, summ <chr>,
## # copy_sign <chr>, bsl_srt <chr>, town_map <chr>, grade_bsl <dbl>,
## # grade_terp <dbl>, comments <chr>names(apt)## [1] "session" "group" "ppt" "nback_lett" "nback_spat"
## [6] "nback_comb" "corsi_bspan" "corsi_score" "corsi_corr" "corsi_mspan"
## [11] "kirk_ceil" "kirk_raw" "kirk_acc" "kbit_ceil" "kbit_raw"
## [16] "kbit_acc" "dspan_mem" "dspan_corr" "dspan_time" "mr2d_acc"
## [21] "mr2d_rt" "mr3d_acc" "mr3d_rt" "bis_tot" "bis_att"
## [26] "bis_mot" "bis_nplan" "mlat_raw" "mlat_acc" "summ"
## [31] "copy_sign" "bsl_srt" "town_map" "grade_bsl" "grade_terp"
## [36] "comments"summary(apt)## session group ppt nback_lett
## Min. :1 Length:99 Length:99 Min. :0.590
## 1st Qu.:1 Class :character Class :character 1st Qu.:0.680
## Median :2 Mode :character Mode :character Median :0.730
## Mean :2 Mean :0.717
## 3rd Qu.:3 3rd Qu.:0.750
## Max. :3 Max. :0.790
## NA's :43
## nback_spat nback_comb corsi_bspan corsi_score
## Min. :0.5200 Min. :0.5700 Min. :5.000 Min. : 30.00
## 1st Qu.:0.7075 1st Qu.:0.7100 1st Qu.:6.000 1st Qu.: 48.00
## Median :0.7300 Median :0.7200 Median :6.000 Median : 54.00
## Mean :0.7250 Mean :0.7207 Mean :6.228 Mean : 57.58
## 3rd Qu.:0.7600 3rd Qu.:0.7500 3rd Qu.:6.000 3rd Qu.: 60.00
## Max. :0.7900 Max. :0.7800 Max. :9.000 Max. :126.00
## NA's :43 NA's :43 NA's :42 NA's :42
## corsi_corr corsi_mspan kirk_ceil kirk_raw
## Min. : 6.000 Min. :4.000 Min. :23.00 Min. :14.00
## 1st Qu.: 8.000 1st Qu.:5.000 1st Qu.:37.00 1st Qu.:29.75
## Median : 9.000 Median :5.500 Median :40.50 Median :31.00
## Mean : 9.053 Mean :5.526 Mean :38.75 Mean :31.11
## 3rd Qu.:10.000 3rd Qu.:6.000 3rd Qu.:42.00 3rd Qu.:34.00
## Max. :14.000 Max. :8.000 Max. :42.00 Max. :39.00
## NA's :42 NA's :42 NA's :43 NA's :43
## kirk_acc kbit_ceil kbit_raw kbit_acc
## Min. :0.4800 Min. :20.00 Min. :16.00 Min. :0.6900
## 1st Qu.:0.7400 1st Qu.:41.00 1st Qu.:37.00 1st Qu.:0.8600
## Median :0.8150 Median :43.00 Median :39.50 Median :0.9250
## Mean :0.8039 Mean :42.25 Mean :38.84 Mean :0.9055
## 3rd Qu.:0.8625 3rd Qu.:45.25 3rd Qu.:41.00 3rd Qu.:0.9600
## Max. :0.9700 Max. :46.00 Max. :46.00 Max. :1.0000
## NA's :43 NA's :43 NA's :43 NA's :43
## dspan_mem dspan_corr dspan_time mr2d_acc
## Min. : 5.000 Min. :0.5000 Min. :2.200 Min. :0.5400
## 1st Qu.: 6.000 1st Qu.:0.6750 1st Qu.:3.735 1st Qu.:0.8600
## Median : 7.500 Median :0.7100 Median :4.540 Median :0.9100
## Mean : 7.367 Mean :0.7287 Mean :4.523 Mean :0.8806
## 3rd Qu.: 8.000 3rd Qu.:0.7975 3rd Qu.:5.095 3rd Qu.:0.9400
## Max. :10.000 Max. :0.8800 Max. :6.790 Max. :0.9800
## NA's :69 NA's :69 NA's :69 NA's :66
## mr2d_rt mr3d_acc mr3d_rt bis_tot
## Min. :1086 Min. :0.1500 Min. :1379 Length:99
## 1st Qu.:2004 1st Qu.:0.5500 1st Qu.:2398 Class :character
## Median :2430 Median :0.7400 Median :3013 Mode :character
## Mean :2761 Mean :0.6916 Mean :2908
## 3rd Qu.:3057 3rd Qu.:0.8400 3rd Qu.:3279
## Max. :8256 Max. :0.9600 Max. :4285
## NA's :66 NA's :62 NA's :62
## bis_att bis_mot bis_nplan mlat_raw
## Length:99 Length:99 Length:99 Min. :13.00
## Class :character Class :character Class :character 1st Qu.:31.75
## Mode :character Mode :character Mode :character Median :38.50
## Mean :35.45
## 3rd Qu.:42.50
## Max. :45.00
## NA's :77
## mlat_acc summ copy_sign bsl_srt
## Min. :0.2900 Length:99 Length:99 Length:99
## 1st Qu.:0.7075 Class :character Class :character Class :character
## Median :0.8550 Mode :character Mode :character Mode :character
## Mean :0.7891
## 3rd Qu.:0.9475
## Max. :1.0000
## NA's :77
## town_map grade_bsl grade_terp comments
## Length:99 Min. :34.00 Min. :44.00 Length:99
## Class :character 1st Qu.:57.25 1st Qu.:51.00 Class :character
## Mode :character Median :62.50 Median :59.50 Mode :character
## Mean :62.83 Mean :60.32
## 3rd Qu.:70.00 3rd Qu.:67.75
## Max. :85.00 Max. :84.00
## NA's :41 NA's :65Summarise missingness
apt %>% select(-session:-ppt, -comments) %>%
naniar::miss_var_summary() # data is in long format though## # A tibble: 32 x 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 bis_tot 83 83.8
## 2 bis_att 83 83.8
## 3 bis_mot 83 83.8
## 4 bis_nplan 83 83.8
## 5 town_map 82 82.8
## 6 bsl_srt 81 81.8
## 7 mlat_raw 77 77.8
## 8 mlat_acc 77 77.8
## 9 summ 76 76.8
## 10 dspan_mem 69 69.7
## # ... with 22 more rowsapt_wide <- apt %>%
tidyr::pivot_wider(names_from = session,
values_from = c(nback_lett:comments))
naniar::miss_var_summary(apt_wide)## # A tibble: 101 x 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 dspan_mem_2 33 100
## 2 dspan_corr_2 33 100
## 3 dspan_time_2 33 100
## 4 bis_tot_1 33 100
## 5 bis_att_1 33 100
## 6 bis_mot_1 33 100
## 7 bis_nplan_1 33 100
## 8 mlat_raw_2 33 100
## 9 mlat_raw_3 33 100
## 10 mlat_acc_2 33 100
## # ... with 91 more rowsvis_miss(apt) # visualise missingness
vis_miss(apt_wide) # visualise missingness
vis_miss(apt, cluster = TRUE) # arrange rows according to missingness
# Show missingness on a row by row basis i.e. by participant & session
gg_miss_case(apt)
Not all of the missingness is a problem, for some variables we did not plan to test on all participants
Test imputation
# simple imputation of replacing all NAs with the mean of that column
imp_mean <- apt_wide %>%
bind_shadow(only_miss = TRUE) %>%
add_label_shadow() %>%
impute_mean_all()## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA
## Warning in mean.default(x, na.rm = TRUE): argument is not numeric or logical:
## returning NA# this keeps the mean of each variable the same (good) but decreases the variance (bad), increasing bias. this method also doesn't take into account other factors that could better predict more plausible values than just using the mean
library(simputation) ##
## Attaching package: 'simputation'## The following object is masked from 'package:naniar':
##
## impute_median# imp_lm <- imp_mean %>% impute_lm(Y ~ X1 + X2)
# not sure how to use this yet
# or use {mice}
library(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindmd.pattern(apt_wide)
## group ppt nback_lett_1 nback_spat_1 nback_comb_1 corsi_bspan_1 corsi_score_1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 3 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1 1
## 8 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 3 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 0 0 0 0 0 0 0
## corsi_corr_1 corsi_mspan_1 kirk_ceil_1 kirk_raw_1 kirk_acc_1 kbit_ceil_1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 3 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1
## 8 1 1 1 1 1 1
## 2 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 3 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 0 0 0 0 0 0
## kbit_raw_1 kbit_acc_1 grade_bsl_2 copy_sign_1 grade_bsl_3 grade_terp_3
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 3 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1
## 8 1 1 1 1 1 1
## 2 1 1 1 1 1 1
## 1 1 1 1 1 1 0
## 2 1 1 1 1 0 0
## 1 1 1 1 1 0 0
## 1 1 1 1 1 0 0
## 1 1 1 1 0 1 1
## 3 1 1 1 0 1 1
## 1 1 1 1 0 1 1
## 1 1 1 0 1 0 0
## 1 1 1 0 0 0 0
## 0 0 2 6 6 7
## mr3d_acc_1 mr3d_rt_1 mlat_raw_1 mlat_acc_1 comments_2 comments_3 summ_1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 3 1 1 1 1 1 1 1
## 1 1 1 1 1 1 0 1
## 1 1 1 1 1 1 0 1
## 2 1 1 1 1 1 0 1
## 8 0 0 0 0 0 1 0
## 2 0 0 0 0 0 0 0
## 1 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1 1
## 1 1 1 1 1 1 0 1
## 1 0 0 0 0 0 1 0
## 1 1 1 1 1 1 1 1
## 3 1 1 1 1 1 0 1
## 1 1 1 1 1 1 0 0
## 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 11 11 11 11 11 11 12
## dspan_mem_1 dspan_corr_1 dspan_time_1 bsl_srt_3 town_map_3 corsi_bspan_2
## 1 1 1 1 1 1 0
## 1 1 1 1 1 1 0
## 1 1 1 1 0 0 0
## 1 1 1 1 0 0 0
## 3 1 1 1 0 0 0
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 0
## 2 1 1 1 1 1 0
## 8 0 0 0 0 0 1
## 2 0 0 0 1 1 1
## 1 1 1 1 0 0 0
## 2 1 1 1 1 1 0
## 1 1 1 1 1 1 0
## 1 0 0 0 0 0 1
## 1 1 1 1 0 0 0
## 3 0 0 0 1 1 0
## 1 1 1 1 1 1 1
## 1 1 1 1 0 0 0
## 1 1 1 1 0 0 0
## 14 14 14 18 18 20
## corsi_score_2 corsi_corr_2 corsi_mspan_2 mr3d_acc_3 mr3d_rt_3 bis_tot_3
## 1 0 0 0 1 1 1
## 1 0 0 0 1 1 1
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 1 1 1 1 1 1 1
## 1 0 0 0 1 1 1
## 2 0 0 0 1 1 1
## 8 1 1 1 0 0 0
## 2 1 1 1 1 1 1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 1 0 0 0 1 1 1
## 1 1 1 1 0 0 0
## 1 0 0 0 0 0 0
## 3 0 0 0 1 1 1
## 1 1 1 1 1 1 1
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 20 20 20 20 20 20
## bis_att_3 bis_mot_3 bis_nplan_3 nback_lett_2 nback_spat_2 nback_comb_2
## 1 1 1 1 0 0 0
## 1 1 1 1 0 0 0
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 1 1 1 1 1 1 1
## 1 1 1 1 0 0 0
## 2 1 1 1 0 0 0
## 8 0 0 0 1 1 1
## 2 1 1 1 1 1 1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 1 1 1 1 0 0 0
## 1 0 0 0 1 1 1
## 1 0 0 0 0 0 0
## 3 1 1 1 0 0 0
## 1 1 1 1 0 0 0
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 20 20 20 21 21 21
## kirk_ceil_3 kirk_raw_3 kirk_acc_3 kbit_ceil_3 kbit_raw_3 kbit_acc_3
## 1 1 1 1 1 1 1
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1
## 8 0 0 0 0 0 0
## 2 1 1 1 1 1 1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 1 1 1 1 1 1 1
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 3 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 21 21 21 21 21 21
## nback_lett_3 nback_spat_3 nback_comb_3 corsi_bspan_3 corsi_score_3
## 1 0 0 0 0 0
## 1 0 0 0 0 0
## 1 0 0 0 0 0
## 1 0 0 0 0 0
## 3 0 0 0 0 0
## 1 1 1 1 1 1
## 1 1 1 1 1 1
## 2 1 1 1 1 1
## 8 0 0 0 0 0
## 2 1 1 1 1 1
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 1 1 1 1 1 1
## 1 0 0 0 0 0
## 1 0 0 0 0 0
## 3 1 1 1 1 1
## 1 1 1 1 1 1
## 1 0 0 0 0 0
## 1 0 0 0 0 0
## 22 22 22 22 22
## corsi_corr_3 corsi_mspan_3 kirk_ceil_2 kirk_raw_2 kirk_acc_2 kbit_ceil_2
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 1 1 1 0 0 0 0
## 1 1 1 0 0 0 0
## 2 1 1 0 0 0 0
## 8 0 0 1 1 1 1
## 2 1 1 1 1 1 1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 1 1 1 0 0 0 0
## 1 0 0 1 1 1 1
## 1 0 0 0 0 0 0
## 3 1 1 0 0 0 0
## 1 1 1 0 0 0 0
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 22 22 22 22 22 22
## kbit_raw_2 kbit_acc_2 dspan_mem_3 dspan_corr_3 dspan_time_3 mr2d_acc_1
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 1 0 0 1 1 1 0
## 1 0 0 1 1 1 0
## 2 0 0 1 1 1 0
## 8 1 1 0 0 0 1
## 2 1 1 1 1 1 1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 1 0 0 1 1 1 0
## 1 1 1 0 0 0 1
## 1 0 0 0 0 0 0
## 3 0 0 1 1 1 0
## 1 0 0 1 1 1 0
## 1 0 0 0 0 0 0
## 1 0 0 0 0 0 0
## 22 22 22 22 22 22
## mr2d_acc_2 mr2d_acc_3 mr2d_rt_1 mr2d_rt_2 mr2d_rt_3 copy_sign_2 grade_terp_2
## 1 0 0 0 0 0 0 1
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 1
## 1 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 1 0 1 0 0 1 0 0
## 1 0 1 0 0 1 0 1
## 2 0 1 0 0 1 0 0
## 8 1 0 1 1 0 1 0
## 2 1 1 1 1 1 1 0
## 1 0 0 0 0 0 0 1
## 2 0 0 0 0 0 0 1
## 1 0 1 0 0 1 0 1
## 1 1 0 1 1 0 1 0
## 1 0 0 0 0 0 0 1
## 3 0 1 0 0 1 0 0
## 1 0 1 0 0 1 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 22 22 22 22 22 22 25
## comments_1 bis_tot_2 bis_att_2 bis_mot_2 bis_nplan_2 bsl_srt_2 mr3d_acc_2
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 1 1 1 1 1 0
## 3 0 0 0 0 0 0 0
## 1 0 1 1 1 1 1 1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0
## 1 1 1 1 1 1 1 1
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 29 30 30 30 30 30 31
## mr3d_rt_2 summ_2 town_map_2 dspan_mem_2 dspan_corr_2 dspan_time_2 bis_tot_1
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 1 1 1 1 0 0 0 0
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 1 1 1 1 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 31 31 31 33 33 33 33
## bis_att_1 bis_mot_1 bis_nplan_1 mlat_raw_2 mlat_raw_3 mlat_acc_2 mlat_acc_3
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0
## 33 33 33 33 33 33 33
## summ_3 copy_sign_3 bsl_srt_1 town_map_1 grade_bsl_1 grade_terp_1
## 1 0 0 0 0 0 0 57
## 1 0 0 0 0 0 0 64
## 1 0 0 0 0 0 0 71
## 1 0 0 0 0 0 0 67
## 3 0 0 0 0 0 0 72
## 1 0 0 0 0 0 0 31
## 1 0 0 0 0 0 0 46
## 2 0 0 0 0 0 0 47
## 8 0 0 0 0 0 0 63
## 2 0 0 0 0 0 0 38
## 1 0 0 0 0 0 0 72
## 2 0 0 0 0 0 0 71
## 1 0 0 0 0 0 0 48
## 1 0 0 0 0 0 0 65
## 1 0 0 0 0 0 0 72
## 3 0 0 0 0 0 0 50
## 1 0 0 0 0 0 0 35
## 1 0 0 0 0 0 0 75
## 1 0 0 0 0 0 0 76
## 33 33 33 33 33 33 1961# impute variables using predictive mean matching (PMM)
#test_mice <- mice(apt_wide, m=5, maxit=50, meth='pmm', seed=500)
#imputed_mice <- mice(apt_wide, m=5, method = "pmm")
# summary(imputed_mice) # check summary
#imputed_mice$imp # check imputed values
# get back to completed data set
#completed_apt <- complete(imputed_mice)
#head(completed_apt)
# certain columns are not fully imputed because they are perfectly collinear with other columns. we can see which columms are collinear:
apt_wide_m <- select(apt_wide, nback_lett_1:mr3d_rt_3,
mlat_raw_1:mlat_acc_3,
grade_bsl_1:grade_terp_3)
apt_wide_m <- as.matrix(apt_wide_m)
#cor(apt_wide_m, use = "pairwise.complete.obs")
#mice:::find.collinear(completed_apt)
#mice(pred = diag(ncol()))
#init = mice(apt_wide, maxit=0)
#meth = init$method
#predM = init$predictorMatrix
# remove specific variable as a predictor for imputation
#predM[c("group")]=0
# or use k-nearest neighbor imputation in {bnstruct}Convert variables
Session and Group should be factors:
apt$session <- as.factor(apt$session)
apt$group <- as.factor(apt$group)
apt$ppt <- as.factor(apt$ppt)
apt$bis_tot <- as.numeric(apt$bis_tot)## Warning: NAs introduced by coercionapt$bis_att <- as.numeric(apt$bis_att)## Warning: NAs introduced by coercionapt$bis_mot <- as.numeric(apt$bis_mot)## Warning: NAs introduced by coercionapt$bis_nplan <- as.numeric(apt$bis_nplan)## Warning: NAs introduced by coercionRename levels of Session:
apt$session <- plyr::revalue(apt$session, c("1" = "pre-degree",
"2" = "1 year degree",
"3" = "2 years degree"))Summary tables
Cognitive & Linguistic predictors
Dual N-Back
apt %>%
filter(!is.na(nback_lett)) %>%
group_by(session) %>%
summarise(nbac_lett_mean = mean(nback_lett),
sd = sd(nback_lett),
se = std.error(nback_lett))## # A tibble: 3 x 4
## session nbac_lett_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 0.712 0.0420 0.00732
## 2 1 year degree 0.704 0.0553 0.0160
## 3 2 years degree 0.745 0.0294 0.00888apt %>%
filter(!is.na(nback_spat)) %>%
group_by(session) %>%
summarise(nbac_spat_mean = mean(nback_spat),
sd = sd(nback_spat),
se = std.error(nback_spat))## # A tibble: 3 x 4
## session nbac_spat_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 0.717 0.0522 0.00909
## 2 1 year degree 0.73 0.0305 0.00879
## 3 2 years degree 0.744 0.0341 0.0103apt %>%
filter(!is.na(nback_comb)) %>%
group_by(session) %>%
summarise(nbac_comb_mean = mean(nback_comb),
sd = sd(nback_comb),
se = std.error(nback_comb))## # A tibble: 3 x 4
## session nbac_comb_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 0.714 0.0398 0.00692
## 2 1 year degree 0.717 0.0368 0.0106
## 3 2 years degree 0.745 0.0250 0.00755Corsi Blocks
apt %>%
filter(!is.na(corsi_bspan)) %>%
group_by(session) %>%
summarise(corsi_bspan_mean = mean(corsi_bspan),
sd = sd(corsi_bspan),
se = std.error(corsi_bspan))## # A tibble: 3 x 4
## session corsi_bspan_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 6.06 1.12 0.194
## 2 1 year degree 6.46 1.13 0.312
## 3 2 years degree 6.45 1.04 0.312apt %>%
filter(!is.na(corsi_score)) %>%
group_by(session) %>%
summarise(corsi_score_mean = mean(corsi_score),
sd = sd(corsi_score),
se = std.error(corsi_score))## # A tibble: 3 x 4
## session corsi_score_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 54.9 19.3 3.36
## 2 1 year degree 64.2 18.6 5.16
## 3 2 years degree 57.9 17.1 5.15apt %>%
filter(!is.na(corsi_corr)) %>%
group_by(session) %>%
summarise(corsi_corr_mean = mean(corsi_corr),
sd = sd(corsi_corr),
se = std.error(corsi_corr))## # A tibble: 3 x 4
## session corsi_corr_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 8.85 1.44 0.250
## 2 1 year degree 9.77 1.30 0.361
## 3 2 years degree 8.82 1.25 0.377apt %>%
filter(!is.na(corsi_mspan)) %>%
group_by(session) %>%
summarise(corsi_mspan_mean = mean(corsi_mspan),
sd = sd(corsi_mspan),
se = std.error(corsi_mspan))## # A tibble: 3 x 4
## session corsi_mspan_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 5.42 0.719 0.125
## 2 1 year degree 5.88 0.650 0.180
## 3 2 years degree 5.41 0.625 0.189Kirklees Sentence Reading
apt %>%
filter(!is.na(kirk_ceil)) %>%
group_by(session) %>%
summarise(kirk_ceil_mean = mean(kirk_ceil),
sd = sd(kirk_ceil),
se = std.error(kirk_ceil))## # A tibble: 3 x 4
## session kirk_ceil_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 38.2 3.97 0.691
## 2 1 year degree 40.1 3.18 0.958
## 3 2 years degree 39 5.66 1.63apt %>%
filter(!is.na(kirk_raw)) %>%
group_by(session) %>%
summarise(kirk_raw_mean = mean(kirk_raw),
sd = sd(kirk_raw),
se = std.error(kirk_raw))## # A tibble: 3 x 4
## session kirk_raw_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 30.7 5.01 0.872
## 2 1 year degree 31 2.57 0.775
## 3 2 years degree 32.4 6.29 1.82apt %>%
filter(!is.na(kirk_acc)) %>%
group_by(session) %>%
summarise(kirk_acc_mean = mean(kirk_acc),
sd = sd(kirk_acc),
se = std.error(kirk_acc))## # A tibble: 3 x 4
## session kirk_acc_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 0.804 0.110 0.0191
## 2 1 year degree 0.775 0.0566 0.0171
## 3 2 years degree 0.829 0.0945 0.0273KBIT-2 Matrices
apt %>%
filter(!is.na(kbit_ceil)) %>%
group_by(session) %>%
summarise(kirk_ceil_mean = mean(kbit_ceil),
sd = sd(kbit_ceil),
se = std.error(kbit_ceil))## # A tibble: 3 x 4
## session kirk_ceil_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 41.3 5.13 0.894
## 2 1 year degree 44.1 2.43 0.732
## 3 2 years degree 43.1 3.12 0.900apt %>%
filter(!is.na(kbit_raw)) %>%
group_by(session) %>%
summarise(kbit_raw_mean = mean(kbit_raw),
sd = sd(kbit_raw),
se = std.error(kbit_raw))## # A tibble: 3 x 4
## session kbit_raw_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 38.0 5.05 0.880
## 2 1 year degree 40.4 2.84 0.856
## 3 2 years degree 39.7 3.42 0.987apt %>%
filter(!is.na(kbit_acc)) %>%
group_by(session) %>%
summarise(kbit_acc_mean = mean(kbit_acc),
sd = sd(kbit_acc),
se = std.error(kbit_acc))## # A tibble: 3 x 4
## session kbit_acc_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 0.908 0.0638 0.0111
## 2 1 year degree 0.88 0.0969 0.0292
## 3 2 years degree 0.922 0.0565 0.0163Digit Span
apt %>%
filter(!is.na(dspan_mem)) %>%
group_by(session) %>%
summarise(dspan_mem_mean = mean(dspan_mem),
sd = sd(dspan_mem),
se = std.error(dspan_mem))## # A tibble: 2 x 4
## session dspan_mem_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 7.16 1.54 0.353
## 2 2 years degree 7.73 1.35 0.407apt %>%
filter(!is.na(dspan_corr)) %>%
group_by(session) %>%
summarise(dspan_corr_mean = mean(dspan_corr),
sd = sd(dspan_corr),
se = std.error(dspan_corr))## # A tibble: 2 x 4
## session dspan_corr_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 0.723 0.0905 0.0208
## 2 2 years degree 0.739 0.0855 0.0258apt %>%
filter(!is.na(dspan_time)) %>%
group_by(session) %>%
summarise(dspan_time_mean = mean(dspan_time),
sd = sd(dspan_time),
se = std.error(dspan_time))## # A tibble: 2 x 4
## session dspan_time_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 4.40 1.16 0.265
## 2 2 years degree 4.73 1.14 0.3442D Mental Rotation
apt %>%
filter(!is.na(mr2d_acc)) %>%
group_by(session) %>%
summarise(mr2d_acc_mean = mean(mr2d_acc),
sd = sd(mr2d_acc),
se = std.error(mr2d_acc))## # A tibble: 3 x 4
## session mr2d_acc_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 0.911 0.0656 0.0198
## 2 1 year degree 0.909 0.0729 0.0220
## 3 2 years degree 0.822 0.118 0.0356apt %>%
filter(!is.na(mr2d_rt)) %>%
group_by(session) %>%
summarise(mr2d_rt_mean = mean(mr2d_rt),
sd = sd(mr2d_rt),
se = std.error(mr2d_rt))## # A tibble: 3 x 4
## session mr2d_rt_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 3419. 1736. 523.
## 2 1 year degree 2493. 1048. 316.
## 3 2 years degree 2373. 906. 273.3D Mental Rotation
apt %>%
filter(!is.na(mr3d_acc)) %>%
group_by(session) %>%
summarise(mr3d_acc_mean = mean(mr3d_acc),
sd = sd(mr3d_acc),
se = std.error(mr3d_acc))## # A tibble: 3 x 4
## session mr3d_acc_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 0.642 0.201 0.0429
## 2 1 year degree 0.795 0.0636 0.045
## 3 2 years degree 0.76 0.148 0.0411apt %>%
filter(!is.na(mr3d_rt)) %>%
group_by(session) %>%
summarise(mr3d_rt_mean = mean(mr3d_rt),
sd = sd(mr3d_rt),
se = std.error(mr3d_rt))## # A tibble: 3 x 4
## session mr3d_rt_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 pre-degree 3217. 594. 127.
## 2 1 year degree 2706. 435. 307.
## 3 2 years degree 2416. 490. 136.Barratt Impulsivity Scale
apt %>%
filter(!is.na(bis_tot)) %>%
group_by(session) %>%
summarise(bis_tot_mean = mean(bis_tot),
sd = sd(bis_tot),
se = std.error(bis_tot))## Warning in mean.default(bis_tot): argument is not numeric or logical: returning
## NA
## Warning in mean.default(bis_tot): argument is not numeric or logical: returning
## NA## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion## # A tibble: 2 x 4
## session bis_tot_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 1 year degree NA NA 6.53
## 2 2 years degree NA 11.8 3.26apt %>%
filter(!is.na(bis_att)) %>%
group_by(session) %>%
summarise(bis_att_mean = mean(bis_att),
sd = sd(bis_att),
se = std.error(bis_att))## Warning in mean.default(bis_att): argument is not numeric or logical: returning
## NA## Warning in mean.default(bis_att): argument is not numeric or logical: returning
## NA## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion## # A tibble: 2 x 4
## session bis_att_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 1 year degree NA NA 2.04
## 2 2 years degree NA 5.35 1.48apt %>%
filter(!is.na(bis_mot)) %>%
group_by(session) %>%
summarise(bis_mot_mean = mean(bis_mot),
sd = sd(bis_mot),
se = std.error(bis_mot))## Warning in mean.default(bis_mot): argument is not numeric or logical: returning
## NA## Warning in mean.default(bis_mot): argument is not numeric or logical: returning
## NA## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion## # A tibble: 2 x 4
## session bis_mot_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 1 year degree NA NA 0.816
## 2 2 years degree NA 5.15 1.43apt %>%
filter(!is.na(bis_nplan)) %>%
group_by(session) %>%
summarise(bis_nplan_mean = mean(bis_nplan),
sd = sd(bis_nplan),
se = std.error(bis_nplan))## Warning in mean.default(bis_nplan): argument is not numeric or logical:
## returning NA## Warning in mean.default(bis_nplan): argument is not numeric or logical:
## returning NA## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion## # A tibble: 2 x 4
## session bis_nplan_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 1 year degree NA NA 3.67
## 2 2 years degree NA 4.94 1.37MLAT Number Learning
insufficient data? only collected at timepoint 1 for grant cohort
Summarising Task
insufficient data? only collected at timepoint 1 for grant cohort
BSL / Interpreting outcome variables
Copy Sign Task
# data to be codedBSL Sentence Reproduction Task
# data to be codedBSL Town Map
# data to be codedBSL Grades
apt %>%
filter(!is.na(grade_bsl)) %>%
group_by(session) %>%
summarise(grade_bsl_mean = mean(grade_bsl),
sd = sd(grade_bsl),
se = std.error(grade_bsl))## # A tibble: 2 x 4
## session grade_bsl_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 1 year degree 63.7 8.37 1.50
## 2 2 years degree 61.8 10.0 1.92Interpreting Grades
apt %>%
filter(!is.na(grade_terp)) %>%
group_by(session) %>%
summarise(grade_terp_mean = mean(grade_terp),
sd = sd(grade_terp),
se = std.error(grade_terp))## # A tibble: 2 x 4
## session grade_terp_mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 1 year degree 64.4 15.6 5.51
## 2 2 years degree 59.1 8.87 1.74Plots
Linguistic & cognitive predictors over time
Dual N-Back
Accuracy in letter matching
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(nback_lett)) %>%
ggplot(aes(x = session, y = nback_lett, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
scale_y_continuous(labels = percent_format(accuracy=1)) +
labs(title = "Accuracy in letter matching during Dual N-Back Task by Session",
x = "Session", y = "Accuracy") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(nback_lett)) %>%
summarize(mean=mean(nback_lett), sd = sd(nback_lett), se=std.error(nback_lett)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
geom_bar(stat = 'identity', position = position_dodge()) +
coord_cartesian(ylim = c(0.5, 0.8)) +
theme_minimal() +
scale_y_continuous(labels = percent_format(accuracy=1)) +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Accuracy in letter matching during Dual N-Back Task by Session",
x = "Session", y = "Accuracy")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5) +
scale_y_continuous(labels = percent_format(accuracy=1)) ## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.indiv <- apt %>%
filter(!is.na(nback_lett)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(nback_lett)) #, sd = sd(nback_lett)) # requires trial level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Accuracy in spatial matching
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(nback_spat)) %>%
ggplot(aes(x = session, y = nback_spat, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Accuracy in spatial matching during Dual N-Back Task by Session",
x = "Session", y = "Accuracy") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(nback_spat)) %>%
summarize(mean=mean(nback_spat), sd = sd(nback_spat), se=std.error(nback_spat)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
geom_bar(stat = 'identity', position = position_dodge()) +
coord_cartesian(ylim = c(0.5, 0.8)) +
theme_minimal() +
scale_y_continuous(labels = percent_format(accuracy=1)) +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Accuracy in spatial matching during Dual N-Back Task by Session",
x = "Session", y = "Accuracy")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5) +
scale_y_continuous(labels = percent_format(accuracy=1)) ## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.indiv <- apt %>%
filter(!is.na(nback_spat)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(nback_spat)) #, sd = sd(nback_spat)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Combined accuracy
## Raincloud plot
apt %>%
filter(!is.na(nback_comb)) %>%
ggplot(aes(x = session, y = nback_comb, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
scale_y_continuous(labels = percent_format(accuracy=1)) +
labs(title = "Combined accuracy on Dual N-Back Task by Session",
x = "Session", y = "Accuracy")
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(nback_comb)) %>%
summarize(mean=mean(nback_comb), sd = sd(nback_comb), se=std.error(nback_comb)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
geom_bar(stat = 'identity', position = position_dodge()) +
coord_cartesian(ylim = c(0.5, 0.8)) +
theme_minimal() +
scale_y_continuous(labels = percent_format(accuracy=1)) +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Combined accuracy on Dual N-Back Task by Session",
x = "Session", y = "Accuracy")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5) +
scale_y_continuous(labels = percent_format(accuracy=1))## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.indiv <- apt %>%
filter(!is.na(nback_comb)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(nback_comb)) #, sd = sd(nback_comb)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Corsi Blocks
Block Span
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(corsi_bspan)) %>%
ggplot(aes(x = session, y = corsi_bspan, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
labs(title = "Block span on Corsi Blocks Task by Session",
x = "Session", y = "Block Span") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(corsi_bspan)) %>%
summarize(mean=mean(corsi_bspan), sd = sd(corsi_bspan), se=std.error(corsi_bspan)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 10)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Block span on Corsi Blocks Task by Session",
x = "Session", y = "Block Span")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(corsi_bspan)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(corsi_bspan)) #, sd = sd(corsi_bspan)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Raw Score
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(corsi_score)) %>%
ggplot(aes(x = session, y = corsi_score, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
labs(title = "Raw Score on Corsi Blocks Task by Session",
x = "Session", y = "Score") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(corsi_score)) %>%
summarize(mean=mean(corsi_score), sd = sd(corsi_score), se=std.error(corsi_score)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 125)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Raw Score on Corsi Blocks Task by Session",
x = "Session", y = "Score")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(corsi_score)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(corsi_score)) #, sd = sd(corsi_score)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Correct Responses
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(corsi_corr)) %>%
ggplot(aes(x = session, y = corsi_corr, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(5, 15)) +
scale_y_continuous(labels = scales::number_format(accuracy = 1)) +
labs(title = "Correct Responses on Corsi Blocks Task by Session",
x = "Session", y = "Correct Response") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(corsi_corr)) %>%
summarize(mean=mean(corsi_corr), sd = sd(corsi_corr), se=std.error(corsi_corr)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 15)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Correct Responses on Corsi Blocks Task by Session",
x = "Session", y = "Correct Response")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(corsi_corr)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(corsi_corr)) #, sd = sd(corsi_corr)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Memory Span
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(corsi_mspan)) %>%
ggplot(aes(x = session, y = corsi_mspan, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
labs(title = "Memory Span on Corsi Blocks Task by Session",
x = "Session", y = "Memory Span") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(corsi_mspan)) %>%
summarize(mean=mean(corsi_mspan), sd = sd(corsi_mspan), se=std.error(corsi_mspan)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 8)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Memory Span on Corsi Blocks Task by Session",
x = "Session", y = "Memory Span")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(corsi_mspan)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(corsi_mspan)) #, sd = sd(corsi_mspan)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Kirklees Sentence Reading
Ceiling Score
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(kirk_ceil)) %>%
ggplot(aes(x = session, y = kirk_ceil, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
labs(title = "Ceiling Score on Kirklees Sentence Reading Task by Session",
x = "Session", y = "Ceiling Score") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(kirk_ceil)) %>%
summarize(mean=mean(kirk_ceil), sd = sd(kirk_ceil), se=std.error(kirk_ceil)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 45)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Ceiling Score on Kirklees Sentence Reading Task by Session",
x = "Session", y = "Ceiling Score")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(kirk_ceil)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(kirk_ceil)) #, sd = sd(kirk_ceil) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Raw Score
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(kirk_raw)) %>%
ggplot(aes(x = session, y = kirk_raw, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
labs(title = "Raw Score on Kirklees Sentence Reading Task by Session",
x = "Session", y = "Ceiling Score") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(kirk_raw)) %>%
summarize(mean=mean(kirk_raw), sd = sd(kirk_raw), se=std.error(kirk_raw)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 45)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Raw Score on Kirklees Sentence Reading Task by Session",
x = "Session", y = "Raw Score")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(kirk_raw)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(kirk_raw)) #, sd = sd(kirk_raw) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Accuracy
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(kirk_acc)) %>%
ggplot(aes(x = session, y = kirk_acc, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(0.45, 1)) +
scale_y_continuous(labels = percent_format(accuracy=1)) +
labs(title = "Accuracy on Kirklees Sentence Reading Task by Session",
x = "Session", y = "Accuracy") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(kirk_acc)) %>%
summarize(mean=mean(kirk_acc), sd = sd(kirk_acc), se=std.error(kirk_acc)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 1)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
scale_y_continuous(labels = percent_format(accuracy=1)) +
labs(title = "Accuracy on Kirklees Sentence Reading Task by Session",
x = "Session", y = "Accuracy")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(kirk_acc)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(kirk_acc)) #, sd = sd(kirk_acc) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
KBIT-2 Matrices
Ceiling Score
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(kbit_ceil)) %>%
ggplot(aes(x = session, y = kbit_ceil, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
labs(title = "Ceiling Score on KBIT-2 Matrices Task by Session",
x = "Session", y = "Ceiling Score") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(kbit_ceil)) %>%
summarize(mean=mean(kbit_ceil), sd = sd(kbit_ceil), se=std.error(kbit_ceil)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 48)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Ceiling Score on KBIT-2 Matrices Task by Session",
x = "Session", y = "Ceiling Score")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(kirk_ceil)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(kbit_ceil)) #, sd = sd(kbit_ceil) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Raw Score
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(kbit_raw)) %>%
ggplot(aes(x = session, y = kbit_raw, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
labs(title = "Raw Score on KBIT-2 Matrices Task by Session",
x = "Session", y = "Ceiling Score") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(kbit_raw)) %>%
summarize(mean=mean(kbit_raw), sd = sd(kbit_raw), se=std.error(kbit_raw)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 45)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Raw Score on KBIT-2 Matrices Task by Session",
x = "Session", y = "Raw Score")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(kbit_raw)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(kbit_raw)) #, sd = sd(kbit_raw) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Accuracy
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(kbit_acc)) %>%
ggplot(aes(x = session, y = kbit_acc, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(.65, 1)) +
scale_y_continuous(labels = percent_format(accuracy=1)) +
labs(title = "Accuracy on KBIT-2 Matrices Task by Session",
x = "Session", y = "Accuracy") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(kbit_acc)) %>%
summarize(mean=mean(kbit_acc),
sd = sd(kbit_acc), se=std.error(kbit_acc)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(.5, 1)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
scale_y_continuous(labels = percent_format(accuracy=1)) +
labs(title = "Accuracy on KBIT-2 Matrices Task by Session",
x = "Session", y = "Accuracy")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(kbit_acc)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(kbit_acc)) #, sd = sd(kbit_acc) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Digit Span
Accuracy
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(dspan_corr)) %>%
ggplot(aes(x = session, y = dspan_corr, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(0.5, 0.9)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Accuracy on Digit Span Task by Session",
x = "Session", y = "Accuracy") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(dspan_corr)) %>%
summarize(mean=mean(dspan_corr), sd = sd(dspan_corr), se=std.error(dspan_corr)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0.5, 0.9)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Accuracy on Digit Span Task by Session",
x = "Session", y = "Accuracy")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(dspan_corr)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(dspan_corr)) #, sd = sd(dspan_corr)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Memory Span
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(dspan_mem)) %>%
ggplot(aes(x = session, y = dspan_mem, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(4, 11)) +
labs(title = "Memory Span on Digit Span Task by Session",
x = "Session", y = "Memory Span") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(dspan_mem)) %>%
summarize(mean=mean(dspan_mem),
sd = sd(dspan_mem), se=std.error(dspan_mem)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 8)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
coord_cartesian(ylim = c(4, 11)) +
labs(title = "Memory Span on Digit Span Task by Session",
x = "Session", y = "Memory Span")## Coordinate system already present. Adding new coordinate system, which will replace the existing one.limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(dspan_mem)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(dspan_mem)) #, sd = sd(dspan_mem)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Time spent
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(dspan_time)) %>%
ggplot(aes(x = session, y = dspan_time, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(0, 8)) +
labs(title = "Time spent on Digit Span Task by Session",
x = "Session", y = "Memory Span") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(dspan_time)) %>%
summarize(mean=mean(dspan_time),
sd = sd(dspan_time), se=std.error(dspan_time)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 8)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
coord_cartesian(ylim = c(0, 8)) +
labs(title = "Time spent on Digit Span Task by Session",
x = "Session", y = "Memory Span")## Coordinate system already present. Adding new coordinate system, which will replace the existing one.limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(dspan_time)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(dspan_time)) #, sd = sd(dspan_time)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
2D Mental Rotation
Accuracy
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(mr2d_acc)) %>%
ggplot(aes(x = session, y = mr2d_acc, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(0.5, 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Accuracy on 2D Mental Rotation Task by Session",
x = "Session", y = "Accuracy") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(mr2d_acc)) %>%
summarize(mean=mean(mr2d_acc), sd = sd(mr2d_acc), se=std.error(mr2d_acc)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0.5, 1)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Accuracy on 2D Mental Rotation Task by Session",
x = "Session", y = "Accuracy")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(mr2d_acc)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(mr2d_acc)) #, sd = sd(mr2d_acc)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Reaction Time
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(mr2d_rt)) %>%
ggplot(aes(x = session, y = mr2d_rt, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(0, 9000)) +
labs(title = "Reaction Time on 2D Mental Rotation Task by Session",
x = "Session", y = "RT (ms)") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(mr2d_rt)) %>%
summarize(mean=mean(mr2d_rt), sd = sd(mr2d_rt), se=std.error(mr2d_rt)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 9000)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Reaction Time on 2D Mental Rotation Task by Session",
x = "Session", y = "RT (ms)")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(mr2d_rt)) %>%
group_by(session, ppt) %>%
summarise(mean = mean(mr2d_rt)) #, sd = sd(mr2d_rt)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
3D Mental Rotation
Accuracy
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(mr3d_acc)) %>%
filter(session != "1 year degree") %>% #only 2 ppts at 2nd session>filter out
ggplot(aes(x = session, y = mr3d_acc, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(0, 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Accuracy on 3D Mental Rotation Task by Session",
x = "Session", y = "Accuracy") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(mr3d_acc)) %>%
filter(session != "1 year degree") %>% #only 2 ppts at 2nd session>filter out
summarize(mean=mean(mr3d_acc), sd = sd(mr3d_acc), se=std.error(mr3d_acc)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 1)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Accuracy on 3D Mental Rotation Task by Session",
x = "Session", y = "Accuracy")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(mr3d_acc)) %>%
filter(session != "1 year degree") %>% #only 2 ppts at 2nd session>filter out
group_by(session, ppt) %>%
summarise(mean = mean(mr3d_acc)) #, sd = sd(mr3d_acc)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Reaction Time
# Raincloud plot
( R1 <- apt %>%
filter(!is.na(mr3d_rt)) %>%
filter(session != "1 year degree") %>% #only 2 ppts at 2nd session>filter out
ggplot(aes(x = session, y = mr3d_rt, group = session)) +
geom_flat_violin(position = position_nudge(x = .25, y = 0),
adjust = 2, trim = TRUE, alpha = 0.3) +
geom_point(position = position_jitter(width = .15),
size = 0.5) +
coord_cartesian(ylim = c(0, 5000)) +
labs(title = "Reaction Time on 3D Mental Rotation Task by Session",
x = "Session", y = "RT (ms)") )
# Jitter plot
J1 <- apt %>%
group_by(session) %>%
filter(!is.na(mr3d_rt)) %>%
filter(session != "1 year degree") %>% #only 2 ppts at 2nd session>filter out
summarize(mean=mean(mr3d_rt), sd = sd(mr3d_rt), se=std.error(mr3d_rt)) %>%
ggplot(aes(x = session, y = mean, fill = session)) +
coord_cartesian(ylim = c(0, 5000)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
scale_fill_manual(values = c("#7bccc4", "#43a2ca", "#0868ac")) +
labs(title = "Reaction Time on 3D Mental Rotation Task by Session",
x = "Session", y = "RT (ms)")
limits <- aes(ymax = mean + se, ymin = mean - se)
J2 <- J1 + geom_errorbar(position = position_dodge(),
limits, size = 0.5, width = 0.5)
indiv <- apt %>%
filter(!is.na(mr3d_rt)) %>%
filter(session != "1 year degree") %>% #only 2 ppts at 2nd session>filter out
group_by(session, ppt) %>%
summarise(mean = mean(mr3d_rt)) #, sd = sd(mr3d_rt)) # requires trial-level data## `summarise()` has grouped output by 'session'. You can override using the `.groups` argument.pd <- position_dodge(0.5)
J3 <- J2 + geom_point(data=indiv, aes(group = ppt), size = 0.8,
shape = 19, position = pd) +
geom_line(data = indiv, aes(group = ppt),
size = 0.1, position = pd)
(J4 <- J3 + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
axis.title.x = element_text(face = "bold"),
legend.position = "none",
axis.title.y = element_text(face = "bold"),
axis.text.x=element_text(size=10, colour="black"),
axis.text.y=element_text(colour="black"))) 
Effects of predictors on outcome variables
Effects of predictors on BSL Grades
Dual N-Back vs BSL Grades
Accuracy in Letter Matching
# Create wider pivot table
nback_lett_grade_bsl_wide <- apt %>%
select(session, group, ppt, nback_lett, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_lett, grade_bsl))
# Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree predict BSL grades at the end of 1st year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 1st year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study predict BSL grades at the end of 1st year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree predict BSL grades at the end of 2nd year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 2nd year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study predict BSL grades at the end of 2nd year?
nback_lett_grade_bsl_wide %>%
ggplot(aes(x = `nback_lett_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Accuracy in Spatial Matching
# Create wider pivot table
nback_spat_grade_bsl_wide <- apt %>%
select(session, group, ppt, nback_spat, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_spat, grade_bsl))
# Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree predict BSL grades at the end of 1st year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task pre-degre")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 1st year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study predict BSL grades at the end of 1st year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree predict BSL grades at the end of 2nd year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 2nd year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study predict BSL grades at the end of 2nd year?
nback_spat_grade_bsl_wide %>%
ggplot(aes(x = `nback_spat_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Combined accuracy
# Create wider pivot table
nback_comb_grade_bsl_wide <- apt %>%
select(session, group, ppt, nback_comb, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_comb, grade_bsl))
# Does Combined Accuracy on the Dual N-Back Task pre-degree predict BSL grades at the end of 1st year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 1st year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 2 years of study predict BSL grades at the end of 1st year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task pre-degree predict BSL grades at the end of 2nd year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 1 year of study predict BSL grades at the end of 2nd year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 2 years of study predict BSL grades at the end of 2nd year?
nback_comb_grade_bsl_wide %>%
ggplot(aes(x = `nback_comb_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Corsi Blocks vs BSL Grades
Block Span
# Create wider pivot table
corsi_bspan_grade_bsl_wide <- apt %>%
select(session, group, ppt, corsi_bspan, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_bspan, grade_bsl))
# Does Block Span on the Corsi Blocks Task pre-degree predict BSL grades at the end of 1st year?
corsi_bspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_bspan_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Block Span on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 1st year?
corsi_bspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_bspan_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Does Block Span on the Corsi Blocks Task after 2 years of study predict BSL grades at the end of 1st year?
corsi_bspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_bspan_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Block Span on the Corsi Blocks Task pre-degree predict BSL grades at the end of 2nd year?
corsi_bspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_bspan_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Block Span on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 2nd year?
corsi_bspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_bspan_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Block Span on the Corsi Blocks Task after 2 years of study predict BSL grades at the end of 2nd year?
corsi_bspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_bspan_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Raw Score
# Create wider pivot table
corsi_score_grade_bsl_wide <- apt %>%
select(session, group, ppt, corsi_score, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_score, grade_bsl))
# Does Raw Score on the Corsi Blocks Task pre-degree predict BSL grades at the end of 1st year?
corsi_score_grade_bsl_wide %>%
ggplot(aes(x = `corsi_score_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Raw Score on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 1st year?
corsi_score_grade_bsl_wide %>%
ggplot(aes(x = `corsi_score_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Does Raw Score on the Corsi Blocks Task after 2 years of study predict BSL grades at the end of 1st year?
corsi_score_grade_bsl_wide %>%
ggplot(aes(x = `corsi_score_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Raw Score on the Corsi Blocks Task pre-degree predict BSL grades at the end of 2nd year?
corsi_score_grade_bsl_wide %>%
ggplot(aes(x = `corsi_score_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Raw Score on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 2nd year?
corsi_score_grade_bsl_wide %>%
ggplot(aes(x = `corsi_score_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Raw Score on the Corsi Blocks Task after 2 years of study predict BSL grades at the end of 2nd year?
corsi_score_grade_bsl_wide %>%
ggplot(aes(x = `corsi_score_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Correct Responses
# Create wider pivot table
corsi_corr_grade_bsl_wide <- apt %>%
select(session, group, ppt, corsi_corr, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_corr, grade_bsl))
# Do Correct Responses on the Corsi Blocks Task pre-degree predict BSL grades at the end of 1st year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 1st year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 2 years of study predict BSL grades at the end of 1st year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task pre-degree predict BSL grades at the end of 2nd year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 2nd year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 2 years of study predict BSL grades at the end of 2nd year?
corsi_corr_grade_bsl_wide %>%
ggplot(aes(x = `corsi_corr_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Memory Span
# Create wider pivot table
corsi_mspan_grade_bsl_wide <- apt %>%
select(session, group, ppt, corsi_mspan, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_mspan, grade_bsl))
# Does Memory Span on the Corsi Blocks Task pre-degree predict BSL grades at the end of 1st year?
corsi_mspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_mspan_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Memory Span on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 1st year?
corsi_mspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_mspan_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Does Memory Span on the Corsi Blocks Task after 2 years of study predict BSL grades at the end of 1st year?
corsi_mspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_mspan_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Memory Span on the Corsi Blocks Task pre-degree predict BSL grades at the end of 2nd year?
corsi_mspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_mspan_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Memory Span on the Corsi Blocks Task after 1 year of study predict BSL grades at the end of 2nd year?
corsi_mspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_mspan_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Memory Span on the Corsi Blocks Task after 2 years of study predict BSL grades at the end of 2nd year?
corsi_mspan_grade_bsl_wide %>%
ggplot(aes(x = `corsi_mspan_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Kirklees Sentence Reading vs BSL Grades
Ceiling Score
# Create wider pivot table
kirk_ceil_grade_bsl_wide <- apt %>%
select(session, group, ppt, kirk_ceil, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kirk_ceil, grade_bsl))
# Does Ceiling Score on the Kirklees Sentence Reading Task pre-degree predict BSL grades at the end of 1st year?
kirk_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kirk_ceil_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task after 1 year of study predict BSL grades at the end of 1st year?
kirk_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kirk_ceil_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task after 2 years of study predict BSL grades at the end of 1st year?
kirk_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kirk_ceil_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task pre-degree predict BSL grades at the end of 2nd year?
kirk_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kirk_ceil_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task after 1 year of study predict BSL grades at the end of 2nd year?
kirk_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kirk_ceil_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task after 2 years of study predict BSL grades at the end of 2nd year?
kirk_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kirk_ceil_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Raw Score
# Create wider pivot table
kirk_raw_grade_bsl_wide <- apt %>%
select(session, group, ppt, kirk_raw, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kirk_raw, grade_bsl))
# Does Raw Score on the Kirklees Sentence Reading Task pre-degree predict BSL grades at the end of 1st year?
kirk_raw_grade_bsl_wide %>%
ggplot(aes(x = `kirk_raw_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task after 1 year of study predict BSL grades at the end of 1st year?
kirk_raw_grade_bsl_wide %>%
ggplot(aes(x = `kirk_raw_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task after 2 years of study predict BSL grades at the end of 1st year?
kirk_raw_grade_bsl_wide %>%
ggplot(aes(x = `kirk_raw_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task pre-degree predict BSL grades at the end of 2nd year?
kirk_raw_grade_bsl_wide %>%
ggplot(aes(x = `kirk_raw_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task after 1 year of study predict BSL grades at the end of 2nd year?
kirk_raw_grade_bsl_wide %>%
ggplot(aes(x = `kirk_raw_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task after 2 years of study predict BSL grades at the end of 2nd year?
kirk_raw_grade_bsl_wide %>%
ggplot(aes(x = `kirk_raw_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Accuracy
# Create wider pivot table
kirk_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, kirk_acc, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kirk_acc, grade_bsl))
# Does Accuracy on the Kirklees Sentence Reading Task pre-degree predict BSL grades at the end of 1st year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study predict BSL grades at the end of 1st year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study predict BSL grades at the end of 1st year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task pre-degree predict BSL grades at the end of 2nd year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study predict BSL grades at the end of 2nd year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study predict BSL grades at the end of 2nd year?
kirk_acc_grade_bsl_wide %>%
ggplot(aes(x = `kirk_acc_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the Kirklees Sentence Reading Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Correlations
kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_pre-degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.433kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_1 year degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_1 year degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.340kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_2 years degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_2 years degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.303kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_pre-degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.108kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_1 year degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_1 year degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.0910kirk_acc_grade_bsl_wide %>%
filter(`kirk_acc_2 years degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_2 years degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.0908KBIT-2 Matrices vs BSL Grades
Ceiling Score
# Create wider pivot table
kbit_ceil_grade_bsl_wide <- apt %>%
select(session, group, ppt, kbit_ceil, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kbit_ceil, grade_bsl))
# Does Ceiling Score on the KBIT-2 Matrices Task pre-degree predict BSL grades at the end of 1st year?
kbit_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kbit_ceil_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task after 1 year of study predict BSL grades at the end of 1st year?
kbit_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kbit_ceil_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task after 2 years of study predict BSL grades at the end of 1st year?
kbit_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kbit_ceil_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task pre-degree predict BSL grades at the end of 2nd year?
kbit_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kbit_ceil_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task after 1 year of study predict BSL grades at the end of 2nd year?
kbit_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kbit_ceil_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task after 2 years of study predict BSL grades at the end of 2nd year?
kbit_ceil_grade_bsl_wide %>%
ggplot(aes(x = `kbit_ceil_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Raw Score
# Create wider pivot table
kbit_raw_grade_bsl_wide <- apt %>%
select(session, group, ppt, kbit_raw, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kbit_raw, grade_bsl))
# Does Raw Score on the KBIT-2 Matrices Task pre-degree predict BSL grades at the end of 1st year?
kbit_raw_grade_bsl_wide %>%
ggplot(aes(x = `kbit_raw_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task after 1 year of study predict BSL grades at the end of 1st year?
kbit_raw_grade_bsl_wide %>%
ggplot(aes(x = `kbit_raw_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task after 2 years of study predict BSL grades at the end of 1st year?
kbit_raw_grade_bsl_wide %>%
ggplot(aes(x = `kbit_raw_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task pre-degree predict BSL grades at the end of 2nd year?
kbit_raw_grade_bsl_wide %>%
ggplot(aes(x = `kbit_raw_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task after 1 year of study predict BSL grades at the end of 2nd year?
kbit_raw_grade_bsl_wide %>%
ggplot(aes(x = `kbit_raw_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task after 2 years of study predict BSL grades at the end of 2nd year?
kbit_raw_grade_bsl_wide %>%
ggplot(aes(x = `kbit_raw_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Accuracy
# Create wider pivot table
kbit_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, kbit_acc, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kbit_acc, grade_bsl))
# Does Accuracy on the KBIT-2 Matrices Task pre-degree predict BSL grades at the end of 1st year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 1 year of study predict BSL grades at the end of 1st year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 2 years of study predict BSL grades at the end of 1st year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task pre-degree predict BSL grades at the end of 2nd year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 1 year of study predict BSL grades at the end of 2nd year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 2 years of study predict BSL grades at the end of 2nd year?
kbit_acc_grade_bsl_wide %>%
ggplot(aes(x = `kbit_acc_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Digit Span vs BSL Grades
Accuracy
# Create wider pivot table
dspan_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, dspan_corr, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(dspan_corr, grade_bsl))
# Does Accuracy on the Digit Span Task pre-degree predict BSL grades at the end of 1st year?
dspan_acc_grade_bsl_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
# Does Accuracy on the Digit Span Task after 1 year of study predict BSL grades at the end of 1st year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Accuracy on the Digit Span Task after 2 years of study predict BSL grades at the end of 1st year?
dspan_acc_grade_bsl_wide %>%
ggplot(aes(x = `dspan_corr_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the Digit Span Task pre-degree predict BSL grades at the end of 2nd year?
dspan_acc_grade_bsl_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 19 rows containing non-finite values (stat_smooth).## Warning: Removed 19 rows containing missing values (geom_point).
# Does Accuracy on the Digit Span Task after 1 year of study predict BSL grades at the end of 2nd year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Accuracy on the Digit Span Task after 2 years of study predict BSL grades at the end of 2nd year?
dspan_acc_grade_bsl_wide %>%
ggplot(aes(x = `dspan_corr_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Memory Span
# Create wider pivot table
dspan_mem_grade_bsl_wide <- apt %>%
select(session, group, ppt, dspan_mem, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(dspan_mem, grade_bsl))
# Does Memory Span on the Digit Span Task pre-degree predict BSL grades at the end of 1st year?
dspan_mem_grade_bsl_wide %>%
ggplot(aes(x = `dspan_mem_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
# Does Memory Span on the Digit Span Task after 1 year of study predict BSL grades at the end of 1st year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Memory Span on the Digit Span Task after 2 years of study predict BSL grades at the end of 1st year?
dspan_mem_grade_bsl_wide %>%
ggplot(aes(x = `dspan_mem_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Memory Span on the Digit Span Task pre-degree predict BSL grades at the end of 2nd year?
dspan_mem_grade_bsl_wide %>%
ggplot(aes(x = `dspan_mem_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 19 rows containing non-finite values (stat_smooth).## Warning: Removed 19 rows containing missing values (geom_point).
# Does Memory Span on the Digit Span Task after 1 year of study predict BSL grades at the end of 2nd year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Memory Span on the Digit Span Task after 2 years of study predict BSL grades at the end of 2nd year?
dspan_mem_grade_bsl_wide %>%
ggplot(aes(x = `dspan_mem_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Create wider pivot table
dspan_time_grade_bsl_wide <- apt %>%
select(session, group, ppt, dspan_time, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(dspan_time, grade_bsl))
# Does Time spent on the Digit Span Task pre-degree predict BSL grades at the end of 1st year?
dspan_time_grade_bsl_wide %>%
ggplot(aes(x = `dspan_time_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Time spent on the Digit Span Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
# Does Time spent on the Digit Span Task after 1 year of study predict BSL grades at the end of 1st year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Time spent on the Digit Span Task after 2 years of study predict BSL grades at the end of 1st year?
dspan_time_grade_bsl_wide %>%
ggplot(aes(x = `dspan_time_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Time spent on the Digit Span Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Time spent on the Digit Span Task pre-degree predict BSL grades at the end of 2nd year?
dspan_time_grade_bsl_wide %>%
ggplot(aes(x = `dspan_time_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Time spent on the Digit Span Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 19 rows containing non-finite values (stat_smooth).## Warning: Removed 19 rows containing missing values (geom_point).
# Does Time spent on the Digit Span Task after 1 year of study predict BSL grades at the end of 2nd year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Time spent on the Digit Span Task after 2 years of study predict BSL grades at the end of 2nd year?
dspan_time_grade_bsl_wide %>%
ggplot(aes(x = `dspan_time_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Time spent on the Digit Span Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
2D Mental Rotation vs BSL Grades
Accuracy
# Create wider pivot table
mr2d_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, mr2d_acc, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr2d_acc, grade_bsl))
# Does Accuracy on the 2D Mental Rotation Task pre-degree predict BSL grades at the end of 1st year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Does Accuracy on the 2D Mental Rotation Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the 2D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 1 year of study predict BSL grades at the end of 1st year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the 2D Mental Rotation Task after 1 year of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).
## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 2 years of study predict BSL grades at the end of 1st year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the 2D Mental Rotation Task after 2 years of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).
## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task pre-degree predict BSL grades at the end of 2nd year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 2D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 1 year of study predict BSL grades at the end of 2nd year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 2D Mental Rotation Task after 1 year of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 2 years of study predict BSL grades at the end of 2nd year?
mr2d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_acc_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 2D Mental Rotation Task after 2 years of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
###### Correlations
mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_pre-degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.128mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_1 year degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_1 year degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.523mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_2 years degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>% summarize(AccCOR=cor(`mr2d_acc_2 years degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.515mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_pre-degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.136mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_1 year degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_1 year degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.722mr2d_acc_grade_bsl_wide %>%
filter(`mr2d_acc_2 years degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>% summarize(AccCOR=cor(`mr2d_acc_2 years degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.288RT
# Create wider pivot table
mr2d_rt_grade_bsl_wide <- apt %>%
select(session, group, ppt, mr2d_rt, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr2d_rt, grade_bsl))
# Does Reaction Time on the 2D Mental Rotation Task pre-degree predict BSL grades at the end of 1st year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 1 year of study predict BSL grades at the end of 1st year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_1 year degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 1 year of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).
## Warning: Removed 22 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 2 years of study predict BSL grades at the end of 1st year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).
## Warning: Removed 22 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task pre-degree predict BSL grades at the end of 2nd year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 1 year of study predict BSL grades at the end of 2nd year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_1 year degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 1 year of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 2 years of study predict BSL grades at the end of 2nd year?
mr2d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr2d_rt_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
Correlations
mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_pre-degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_pre-degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.550mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_1 year degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_1 year degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.473mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_2 years degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>% summarize(rtCOR=cor(`mr2d_rt_2 years degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.230mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_pre-degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_pre-degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.100mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_1 year degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_1 year degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.219mr2d_rt_grade_bsl_wide %>%
filter(`mr2d_rt_2 years degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>% summarize(rtCOR=cor(`mr2d_rt_2 years degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.3263D Mental Rotation vs BSL Grades
Accuracy
# Create wider pivot table
mr3d_acc_grade_bsl_wide <- apt %>%
select(session, group, ppt, mr3d_acc, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr3d_acc, grade_bsl))
# Does Accuracy on the 3D Mental Rotation Task pre-degree predict BSL grades at the end of 1st year?
mr3d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_acc_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 3D Mental Rotation Task pre-degree\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the 3D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).
# Does Accuracy on the 3D Mental Rotation Task after 1 year of study predict BSL grades at the end of 1st year?
# mr3d_acc_grade_bsl_wide %>%
# ggplot(aes(x = `mr3d_acc_1 year degree`, y = `grade_bsl_1 year degree`)) +
# geom_point() +
# geom_smooth(method= "lm") +
# theme_minimal() # removing bc only 2 datapoints for 3DMR @ timepoint 2
# Does Accuracy on the 3D Mental Rotation Task after 2 years of study predict BSL grades at the end of 1st year?
mr3d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_acc_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Accuracy on the 3D Mental Rotation Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Does Accuracy on the 3D Mental Rotation Task pre-degree predict BSL grades at the end of 2nd year?
mr3d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_acc_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
# Does Accuracy on the 3D Mental Rotation Task after 1 year of study predict BSL grades at the end of 2nd year?
# mr3d_acc_grade_bsl_wide %>%
# ggplot(aes(x = `mr3d_acc_1 year degree`, y = `grade_bsl_2 years degree`)) +
# geom_point() +
# geom_smooth(method= "lm") +
# theme_minimal() # removing bc only 2 datapoints for 3DMR @ timepoint 2
# Does Accuracy on the 3D Mental Rotation Task after 2 years of study predict BSL grades at the end of 2nd year?
mr3d_acc_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_acc_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Accuracy on the 3D Mental Rotation Task after 2 years") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
###### Correlations
mr3d_acc_grade_bsl_wide %>%
filter(`mr3d_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_pre-degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.266mr3d_acc_grade_bsl_wide %>%
filter(`mr3d_acc_2 years degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>% summarize(AccCOR=cor(`mr3d_acc_2 years degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.707mr3d_acc_grade_bsl_wide %>%
filter(`mr3d_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_pre-degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.114mr3d_acc_grade_bsl_wide %>%
filter(`mr3d_acc_2 years degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>% summarize(AccCOR=cor(`mr3d_acc_2 years degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.604RT
# Create wider pivot table
mr3d_rt_grade_bsl_wide <- apt %>%
select(session, group, ppt, mr3d_rt, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr3d_rt, grade_bsl))
# Does Reaction Time on the 3D Mental Rotation Task pre-degree predict BSL grades at the end of 1st year?
mr3d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_rt_pre-degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Reaction Time on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).
# Does Reaction Time on the 3D Mental Rotation Task after 1 year of study predict BSL grades at the end of 1st year?
# mr3d_rt_grade_bsl_wide %>%
# ggplot(aes(x = `mr3d_rt_1 year degree`, y = `grade_bsl_1 year degree`)) +
# geom_point() +
# geom_smooth(method= "lm") +
# theme_minimal() # removing bc only 2 datapoints for 3DMR @ timepoint 2
# Does Reaction Time on the 3D Mental Rotation Task after 2 years of study predict BSL grades at the end of 1st year?
mr3d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_rt_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 1st year?", y = "1st year BSL grade", x = "Reaction Time on the 3D Mental Rotation Task after 2 year of study") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Does Reaction Time on the 3D Mental Rotation Task pre-degree predict BSL grades at the end of 2nd year?
mr3d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_rt_pre-degree`, y = `grade_bsl_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task pre-degree\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Reaction Time on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
# Does Reaction Time on the 3D Mental Rotation Task after 1 year of study predict BSL grades at the end of 2nd year?
# mr3d_rt_grade_bsl_wide %>%
# ggplot(aes(x = `mr3d_rt_1 year degree`, y = `grade_bsl_2 years degree`)) +
# geom_point() +
# geom_smooth(method= "lm") +
# theme_minimal() # removing bc only 2 datapoints for 3DMR @ timepoint 2
# Does Reaction Time on the 3D Mental Rotation Task after 2 years of study predict BSL grades at the end of 2nd year?
mr3d_rt_grade_bsl_wide %>%
ggplot(aes(x = `mr3d_rt_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Reaction Time on the 3D Mental Rotation Task after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = "Reaction Time on the 3D Mental Rotation Task after 2 years of study") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
###### Correlations
mr3d_rt_grade_bsl_wide %>%
filter(`mr3d_rt_pre-degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_pre-degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.136mr3d_rt_grade_bsl_wide %>%
filter(`mr3d_rt_2 years degree`!= "NA") %>%
filter(`grade_bsl_1 year degree` != "NA") %>% summarize(rtCOR=cor(`mr3d_rt_2 years degree`,
`grade_bsl_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.138mr3d_rt_grade_bsl_wide %>%
filter(`mr3d_rt_pre-degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_pre-degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.164mr3d_rt_grade_bsl_wide %>%
filter(`mr3d_rt_2 years degree`!= "NA") %>%
filter(`grade_bsl_2 years degree` != "NA") %>% summarize(rtCOR=cor(`mr3d_rt_2 years degree`,
`grade_bsl_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.294Barratt Impulsiveness Scale vs BSL Grades
Total Impulsiveness
# Create wider pivot table
bis_tot_grade_bsl_wide <- apt %>%
select(session, group, ppt, bis_tot, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_tot, grade_bsl))
# Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study predict BSL grades at the end of 1st year?
bis_tot_grade_bsl_wide %>%
filter(`bis_tot_2 years degree` != "na") %>%
filter(`grade_bsl_1 year degree` != "na") %>%
ggplot(aes(x = `bis_tot_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'
# Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study predict BSL grades at the end of 2nd year?
bis_tot_grade_bsl_wide %>%
filter(`bis_tot_2 years degree` != "na") %>%
filter(`grade_bsl_2 years degree` != "na") %>%
ggplot(aes(x = `bis_tot_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'
Not sure why geom_smooth() line does not appear in knitted output (works in .Rmd file)
Attentional Impulsiveness
# Create wider pivot table
bis_att_grade_bsl_wide <- apt %>%
select(session, group, ppt, bis_att, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_att, grade_bsl))
# Does Attentional Impulsiveness after 2 years predict BSL grades at the end of 1st year?
bis_att_grade_bsl_wide %>%
filter(`bis_att_2 years degree` != "na") %>%
ggplot(aes(x = `bis_att_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Attentional Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'
# Does Attentional Impulsiveness after 2 years predict BSL grades at the end of 2nd year?
bis_att_grade_bsl_wide %>%
filter(`bis_att_2 years degree` != "na") %>%
ggplot(aes(x = `bis_att_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Attentional Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
Motor Impulsiveness
# Create wider pivot table
bis_nplan_grade_bsl_wide <- apt %>%
select(session, group, ppt, bis_nplan, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_nplan, grade_bsl))
# Does Motor Impulsiveness on the Barratt Impulsiveness Scale after 2 years predict BSL grades at the end of 1st year?
bis_nplan_grade_bsl_wide %>%
filter(`bis_nplan_2 years degree` != "na") %>%
ggplot(aes(x = `bis_nplan_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Motor Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'
# Does Motor Impulsiveness on the Barratt Impulsiveness Scale after 2 years predict BSL grades at the end of 2nd year?
bis_nplan_grade_bsl_wide %>%
filter(`bis_nplan_2 years degree` != "na") %>%
ggplot(aes(x = `bis_nplan_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Motor Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
Non-Planning Impulsiveness
# Create wider pivot table
bis_mot_grade_bsl_wide <- apt %>%
select(session, group, ppt, bis_mot, grade_bsl) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_mot, grade_bsl))
# Does Non-PLanning Impulsiveness on the Barratt Impulsiveness Scale after 2 years predict BSL grades at the end of 1st year?
bis_mot_grade_bsl_wide %>%
filter(`bis_mot_2 years degree` != "NA") %>%
ggplot(aes(x = `bis_mot_2 years degree`, y = `grade_bsl_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Non-Planning Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'
# Does Non-PLanning Impulsiveness on the Barratt Impulsiveness Scale after 2 years predict BSL grades at the end of 2nd year?
bis_mot_grade_bsl_wide %>%
filter(`bis_mot_2 years degree` != "na") %>%
ggplot(aes(x = `bis_mot_2 years degree`, y = `grade_bsl_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Non-Planning Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict BSL grades at the end of 2nd year?", y = "2nd year BSL grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
Effects of predictors on Interpreting Grades
Dual N-Back vs Interpreting Grades
Accuracy in Letter Matching
# Create wider pivot table
nback_lett_grade_terp_wide <- apt %>%
select(session, group, ppt, nback_lett, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_lett, grade_terp))
# Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 1st year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study predict Interpreting Grades at the end of 1st year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study predict Interpreting Grades at the end of 1st year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 2nd year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
nback_lett_grade_terp_wide %>%
ggplot(aes(x = `nback_lett_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Letter Matching on the Dual N-Back Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Letter Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Accuracy in Spatial Matching
# Create wider pivot table
nback_spat_grade_terp_wide <- apt %>%
select(session, group, ppt, nback_spat, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_spat, grade_terp))
# Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 1st year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task pre-degre")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study predict Interpreting Grades at the end of 1st year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study predict Interpreting Grades at the end of 1st year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 2nd year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
nback_spat_grade_terp_wide %>%
ggplot(aes(x = `nback_spat_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy in Spatial Matching on the Dual N-Back Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy in Spatial Matching on the Dual N-Back Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Combined accuracy
# Create wider pivot table
nback_comb_grade_terp_wide <- apt %>%
select(session, group, ppt, nback_comb, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(nback_comb, grade_terp))
# Does Combined Accuracy on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 1st year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 1 year of study predict Interpreting Grades at the end of 1st year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 2 years of study predict Interpreting Grades at the end of 1st year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Combined Accuracy on the Dual N-Back Task pre-degree predict Interpreting Grades at the end of 2nd year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
# Does Combined Accuracy on the Dual N-Back Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
nback_comb_grade_terp_wide %>%
ggplot(aes(x = `nback_comb_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Combined Accuracy on the Dual N-Back Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
###### Correlations
nback_comb_grade_terp_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_pre-degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.111nback_comb_grade_terp_wide %>%
filter(`nback_comb_1 year degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_1 year degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 NAnback_comb_grade_terp_wide %>%
filter(`nback_comb_2 years degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>% summarize(AccCOR=cor(`nback_comb_2 years degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -1nback_comb_grade_terp_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_pre-degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.328nback_comb_grade_terp_wide %>%
filter(`nback_comb_1 year degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`nback_comb_1 year degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.526nback_comb_grade_terp_wide %>%
filter(`nback_comb_2 years degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>% summarize(AccCOR=cor(`nback_comb_2 years degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.0265Corsi Blocks vs Interpreting Grades
Block Span
# Create wider pivot table
corsi_bspan_grade_terp_wide <- apt %>%
select(session, group, ppt, corsi_bspan, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_bspan, grade_terp))
# Does Block Span on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 1st year?
corsi_bspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_bspan_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Block Span on the Corsi Blocks Task after 1 year of study predict Interpreting Grades at the end of 1st year?
corsi_bspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_bspan_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Block Span on the Corsi Blocks Task after 2 years of study predict Interpreting Grades at the end of 1st year?
corsi_bspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_bspan_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Block Span on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 2nd year?
corsi_bspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_bspan_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Block Span on the Corsi Blocks Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
corsi_bspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_bspan_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Block Span on the Corsi Blocks Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
corsi_bspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_bspan_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Block Span on the Corsi Blocks Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Raw Score
# Create wider pivot table
corsi_score_grade_terp_wide <- apt %>%
select(session, group, ppt, corsi_score, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_score, grade_terp))
# Does Raw Score on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 1st year?
corsi_score_grade_terp_wide %>%
ggplot(aes(x = `corsi_score_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Raw Score on the Corsi Blocks Task after 1 year of study predict Interpreting Grades at the end of 1st year?
corsi_score_grade_terp_wide %>%
ggplot(aes(x = `corsi_score_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Raw Score on the Corsi Blocks Task after 2 years of study predict Interpreting Grades at the end of 1st year?
corsi_score_grade_terp_wide %>%
ggplot(aes(x = `corsi_score_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Raw Score on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 2nd year?
corsi_score_grade_terp_wide %>%
ggplot(aes(x = `corsi_score_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Raw Score on the Corsi Blocks Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
corsi_score_grade_terp_wide %>%
ggplot(aes(x = `corsi_score_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Raw Score on the Corsi Blocks Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
corsi_score_grade_terp_wide %>%
ggplot(aes(x = `corsi_score_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Corsi Blocks Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Correct Responses
# Create wider pivot table
corsi_corr_grade_terp_wide <- apt %>%
select(session, group, ppt, corsi_corr, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_corr, grade_terp))
# Do Correct Responses on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 1st year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 1 year of study predict Interpreting Grades at the end of 1st year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 2 years of study predict Interpreting Grades at the end of 1st year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Do Correct Responses on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 2nd year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Do Correct Responses on the Corsi Blocks Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
corsi_corr_grade_terp_wide %>%
ggplot(aes(x = `corsi_corr_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Do Correct Responses on the Corsi Blocks Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Memory Span
# Create wider pivot table
corsi_mspan_grade_terp_wide <- apt %>%
select(session, group, ppt, corsi_mspan, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(corsi_mspan, grade_terp))
# Does Memory Span on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 1st year?
corsi_mspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_mspan_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Memory Span on the Corsi Blocks Task after 1 year of study predict Interpreting Grades at the end of 1st year?
corsi_mspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_mspan_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Memory Span on the Corsi Blocks Task after 2 years of study predict Interpreting Grades at the end of 1st year?
corsi_mspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_mspan_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Memory Span on the Corsi Blocks Task pre-degree predict Interpreting Grades at the end of 2nd year?
corsi_mspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_mspan_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Memory Span on the Corsi Blocks Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
corsi_mspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_mspan_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
# Does Memory Span on the Corsi Blocks Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
corsi_mspan_grade_terp_wide %>%
ggplot(aes(x = `corsi_mspan_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Corsi Blocks Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Kirklees Sentence Reading vs Interpreting Grades
Ceiling Score
# Create wider pivot table
kirk_ceil_grade_terp_wide <- apt %>%
select(session, group, ppt, kirk_ceil, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kirk_ceil, grade_terp))
# Does Ceiling Score on the Kirklees Sentence Reading Task pre-degree predict Interpreting Grades at the end of 1st year?
kirk_ceil_grade_terp_wide %>%
ggplot(aes(x = `kirk_ceil_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task after 1 year of study predict Interpreting Grades at the end of 1st year?
kirk_ceil_grade_terp_wide %>%
ggplot(aes(x = `kirk_ceil_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task after 2 years of study predict Interpreting Grades at the end of 1st year?
kirk_ceil_grade_terp_wide %>%
ggplot(aes(x = `kirk_ceil_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing non-finite values (stat_smooth).## Warning: Removed 30 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task pre-degree predict Interpreting Grades at the end of 2nd year?
kirk_ceil_grade_terp_wide %>%
ggplot(aes(x = `kirk_ceil_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
kirk_ceil_grade_terp_wide %>%
ggplot(aes(x = `kirk_ceil_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Ceiling Score on the Kirklees Sentence Reading Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
kirk_ceil_grade_terp_wide %>%
ggplot(aes(x = `kirk_ceil_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the Kirklees Sentence Reading Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Raw Score
# Create wider pivot table
kirk_raw_grade_terp_wide <- apt %>%
select(session, group, ppt, kirk_raw, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kirk_raw, grade_terp))
# Does Raw Score on the Kirklees Sentence Reading Task pre-degree predict Interpreting Grades at the end of 1st year?
kirk_raw_grade_terp_wide %>%
ggplot(aes(x = `kirk_raw_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task after 1 year of study predict Interpreting Grades at the end of 1st year?
kirk_raw_grade_terp_wide %>%
ggplot(aes(x = `kirk_raw_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task after 2 years of study predict Interpreting Grades at the end of 1st year?
kirk_raw_grade_terp_wide %>%
ggplot(aes(x = `kirk_raw_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing non-finite values (stat_smooth).## Warning: Removed 30 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task pre-degree predict Interpreting Grades at the end of 2nd year?
kirk_raw_grade_terp_wide %>%
ggplot(aes(x = `kirk_raw_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
kirk_raw_grade_terp_wide %>%
ggplot(aes(x = `kirk_raw_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Raw Score on the Kirklees Sentence Reading Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
kirk_raw_grade_terp_wide %>%
ggplot(aes(x = `kirk_raw_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the Kirklees Sentence Reading Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Accuracy
# Create wider pivot table
kirk_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, kirk_acc, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kirk_acc, grade_terp))
# Does Accuracy on the Kirklees Sentence Reading Task pre-degree predict Interpreting Grades at the end of 1st year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study predict Interpreting Grades at the end of 1st year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study predict Interpreting Grades at the end of 1st year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing non-finite values (stat_smooth).## Warning: Removed 30 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task pre-degree predict Interpreting Grades at the end of 2nd year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task after 1 year")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
kirk_acc_grade_terp_wide %>%
ggplot(aes(x = `kirk_acc_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the Kirklees Sentence Reading Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the Kirklees Sentence Reading Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Correlations
kirk_acc_grade_terp_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_pre-degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.400kirk_acc_grade_terp_wide %>%
filter(`kirk_acc_1 year degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_1 year degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 NAkirk_acc_grade_terp_wide %>%
filter(`kirk_acc_2 years degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_2 years degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.637kirk_acc_grade_terp_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_pre-degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.305kirk_acc_grade_terp_wide %>%
filter(`kirk_acc_1 year degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_1 year degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.225kirk_acc_grade_terp_wide %>%
filter(`kirk_acc_2 years degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`kirk_acc_2 years degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.214KBIT-2 Matrices vs Interpreting Grades
Ceiling Score
# Create wider pivot table
kbit_ceil_grade_terp_wide <- apt %>%
select(session, group, ppt, kbit_ceil, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kbit_ceil, grade_terp))
# Does Ceiling Score on the KBIT-2 Matrices Task pre-degree predict Interpreting Grades at the end of 1st year?
kbit_ceil_grade_terp_wide %>%
ggplot(aes(x = `kbit_ceil_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task after 1 year of study predict Interpreting Grades at the end of 1st year?
kbit_ceil_grade_terp_wide %>%
ggplot(aes(x = `kbit_ceil_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task after 2 years of study predict Interpreting Grades at the end of 1st year?
kbit_ceil_grade_terp_wide %>%
ggplot(aes(x = `kbit_ceil_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing non-finite values (stat_smooth).## Warning: Removed 30 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task pre-degree predict Interpreting Grades at the end of 2nd year?
kbit_ceil_grade_terp_wide %>%
ggplot(aes(x = `kbit_ceil_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
kbit_ceil_grade_terp_wide %>%
ggplot(aes(x = `kbit_ceil_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Ceiling Score on the KBIT-2 Matrices Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
kbit_ceil_grade_terp_wide %>%
ggplot(aes(x = `kbit_ceil_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Ceiling Score on the KBIT-2 Matrices Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Raw Score
# Create wider pivot table
kbit_raw_grade_terp_wide <- apt %>%
select(session, group, ppt, kbit_raw, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kbit_raw, grade_terp))
# Does Raw Score on the KBIT-2 Matrices Task pre-degree predict Interpreting Grades at the end of 1st year?
kbit_raw_grade_terp_wide %>%
ggplot(aes(x = `kbit_raw_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task after 1 year of study predict Interpreting Grades at the end of 1st year?
kbit_raw_grade_terp_wide %>%
ggplot(aes(x = `kbit_raw_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task after 2 years of study predict Interpreting Grades at the end of 1st year?
kbit_raw_grade_terp_wide %>%
ggplot(aes(x = `kbit_raw_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing non-finite values (stat_smooth).## Warning: Removed 30 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task pre-degree predict Interpreting Grades at the end of 2nd year?
kbit_raw_grade_terp_wide %>%
ggplot(aes(x = `kbit_raw_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
kbit_raw_grade_terp_wide %>%
ggplot(aes(x = `kbit_raw_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Raw Score on the KBIT-2 Matrices Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
kbit_raw_grade_terp_wide %>%
ggplot(aes(x = `kbit_raw_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Raw Score on the KBIT-2 Matrices Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Accuracy
# Create wider pivot table
kbit_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, kbit_acc, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(kbit_acc, grade_terp))
# Does Accuracy on the KBIT-2 Matrices Task pre-degree predict Interpreting Grades at the end of 1st year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 1 year of study predict Interpreting Grades at the end of 1st year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 2 years of study predict Interpreting Grades at the end of 1st year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing non-finite values (stat_smooth).## Warning: Removed 30 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task pre-degree predict Interpreting Grades at the end of 2nd year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the KBIT-2 Matrices Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
kbit_acc_grade_terp_wide %>%
ggplot(aes(x = `kbit_acc_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the KBIT-2 Matrices Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
Digit Span vs Interpreting Grades
Accuracy
# Create wider pivot table
dspan_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, dspan_corr, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(dspan_corr, grade_terp))
# Does Accuracy on the Digit Span Task pre-degree predict Interpreting Grades at the end of 1st year?
dspan_acc_grade_terp_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy on the Digit Span Task after 1 year of study predict Interpreting Grades at the end of 1st year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Accuracy on the Digit Span Task after 2 years of study predict Interpreting Grades at the end of 1st year?
dspan_acc_grade_terp_wide %>%
ggplot(aes(x = `dspan_corr_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Accuracy on the Digit Span Task pre-degree predict Interpreting Grades at the end of 2nd year?
dspan_acc_grade_terp_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Does Accuracy on the Digit Span Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Accuracy on the Digit Span Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
dspan_acc_grade_terp_wide %>%
ggplot(aes(x = `dspan_corr_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Accuracy on the Digit Span Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Correlations
dspan_acc_grade_terp_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`dspan_corr_pre-degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.124dspan_acc_grade_terp_wide %>%
filter(`dspan_corr_2 years degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`dspan_corr_2 years degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -1dspan_acc_grade_terp_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`dspan_corr_pre-degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.581dspan_acc_grade_terp_wide %>%
filter(`dspan_corr_2 years degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`dspan_corr_2 years degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.266Memory Span
# Create wider pivot table
dspan_mem_grade_terp_wide <- apt %>%
select(session, group, ppt, dspan_mem, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(dspan_mem, grade_terp))
# Does Memory Span on the Digit Span Task pre-degree predict Interpreting Grades at the end of 1st year?
dspan_mem_grade_terp_wide %>%
ggplot(aes(x = `dspan_mem_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Memory Span on the Digit Span Task after 1 year of study predict Interpreting Grades at the end of 1st year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Memory Span on the Digit Span Task after 2 years of study predict Interpreting Grades at the end of 1st year?
dspan_mem_grade_terp_wide %>%
ggplot(aes(x = `dspan_mem_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Memory Span on the Digit Span Task pre-degree predict Interpreting Grades at the end of 2nd year?
dspan_mem_grade_terp_wide %>%
ggplot(aes(x = `dspan_mem_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Does Memory Span on the Digit Span Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Memory Span on the Digit Span Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
dspan_mem_grade_terp_wide %>%
ggplot(aes(x = `dspan_mem_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Memory Span on the Digit Span Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Create wider pivot table
dspan_time_grade_terp_wide <- apt %>%
select(session, group, ppt, dspan_time, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(dspan_time, grade_terp))
# Does Time spent on the Digit Span Task pre-degree predict Interpreting Grades at the end of 1st year?
dspan_time_grade_terp_wide %>%
ggplot(aes(x = `dspan_time_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Time spent on the Digit Span Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Time spent on the Digit Span Task after 1 year of study predict Interpreting Grades at the end of 1st year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Time spent on the Digit Span Task after 2 years of study predict Interpreting Grades at the end of 1st year?
dspan_time_grade_terp_wide %>%
ggplot(aes(x = `dspan_time_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Time spent on the Digit Span Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Time spent on the Digit Span Task pre-degree predict Interpreting Grades at the end of 2nd year?
dspan_time_grade_terp_wide %>%
ggplot(aes(x = `dspan_time_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Time spent on the Digit Span Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
# Does Time spent on the Digit Span Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
# removing bc no datapoints for Digit Span @ timepoint 2
# Does Time spent on the Digit Span Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
dspan_time_grade_terp_wide %>%
ggplot(aes(x = `dspan_time_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Time spent on the Digit Span Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
2D Mental Rotation vs Interpreting Grades
Accuracy
# Create wider pivot table
mr2d_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, mr2d_acc, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr2d_acc, grade_terp))
# Does Accuracy on the 2D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 1st year?
mr2d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr2d_acc_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
labs(title = "Does Accuracy on the 2D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the 2D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 1st year?
mr2d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr2d_acc_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the 2D Mental Rotation Task after 1 year of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).
## Warning: Removed 33 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 1st year?
mr2d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr2d_acc_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the 2D Mental Rotation Task after 2 years of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Accuracy on the 2D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 2nd year?
mr2d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr2d_acc_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 2D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
mr2d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr2d_acc_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 2D Mental Rotation Task after 1 year of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
# Does Accuracy on the 2D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
mr2d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr2d_acc_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 2D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 2D Mental Rotation Task after 2 years of study")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
###### Correlations
mr2d_acc_grade_terp_wide %>%
filter(`mr2d_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_pre-degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 NAmr2d_acc_grade_terp_wide %>%
filter(`mr2d_acc_1 year degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_1 year degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 NAmr2d_acc_grade_terp_wide %>%
filter(`mr2d_acc_2 years degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>% summarize(AccCOR=cor(`mr2d_acc_2 years degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 1mr2d_acc_grade_terp_wide %>%
filter(`mr2d_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_pre-degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.0795mr2d_acc_grade_terp_wide %>%
filter(`mr2d_acc_1 year degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`mr2d_acc_1 year degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.614mr2d_acc_grade_terp_wide %>%
filter(`mr2d_acc_2 years degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>% summarize(AccCOR=cor(`mr2d_acc_2 years degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.288RT
# Create wider pivot table
mr2d_rt_grade_terp_wide <- apt %>%
select(session, group, ppt, mr2d_rt, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr2d_rt, grade_terp))
# Does Reaction Time on the 2D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 1st year?
mr2d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr2d_rt_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).## Warning: Removed 33 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 1st year?
mr2d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr2d_rt_1 year degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 1 year of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 33 rows containing non-finite values (stat_smooth).
## Warning: Removed 33 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 1st year?
mr2d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr2d_rt_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning in qt((1 - level)/2, df): NaNs produced## Warning: Removed 31 rows containing missing values (geom_point).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Does Reaction Time on the 2D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 2nd year?
mr2d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr2d_rt_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
mr2d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr2d_rt_1 year degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 1 year of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
# Does Reaction Time on the 2D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
mr2d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr2d_rt_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Does Reaction Time on the 2D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).
## Warning: Removed 23 rows containing missing values (geom_point).
Correlations
mr2d_rt_grade_terp_wide %>%
filter(`mr2d_rt_pre-degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_pre-degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 NAmr2d_rt_grade_terp_wide %>%
filter(`mr2d_rt_1 year degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_1 year degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 NAmr2d_rt_grade_terp_wide %>%
filter(`mr2d_rt_2 years degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>% summarize(rtCOR=cor(`mr2d_rt_2 years degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -1mr2d_rt_grade_terp_wide %>%
filter(`mr2d_rt_pre-degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_pre-degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.239mr2d_rt_grade_terp_wide %>%
filter(`mr2d_rt_1 year degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(rtCOR=cor(`mr2d_rt_1 year degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.107mr2d_rt_grade_terp_wide %>%
filter(`mr2d_rt_2 years degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>% summarize(rtCOR=cor(`mr2d_rt_2 years degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.1393D Mental Rotation vs Interpreting Grades
Accuracy
# Create wider pivot table
mr3d_acc_grade_terp_wide <- apt %>%
select(session, group, ppt, mr3d_acc, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr3d_acc, grade_terp))
# Does Accuracy on the 3D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 1st year?
mr3d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr3d_acc_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Does Accuracy on the 3D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the 3D Mental Rotation Task pre-degree")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Accuracy on the 3D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 1st year?
# mr3d_acc_grade_terp_wide %>%
# ggplot(aes(x = `mr3d_acc_1 year degree`, y = `grade_terp_1 year degree`)) +
# geom_point() +
# geom_smooth(method= "lm") +
# theme_minimal() # removing bc only 2 datapoints for 3DMR @ timepoint 2
# Does Accuracy on the 3D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 1st year?
mr3d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr3d_acc_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Accuracy on the 3D Mental Rotation Task after 2 years")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing non-finite values (stat_smooth).## Warning: Removed 30 rows containing missing values (geom_point).
# Does Accuracy on the 3D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 2nd year?
mr3d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr3d_acc_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 17 rows containing non-finite values (stat_smooth).## Warning: Removed 17 rows containing missing values (geom_point).
# Does Accuracy on the 3D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
# mr3d_acc_grade_terp_wide %>%
# ggplot(aes(x = `mr3d_acc_1 year degree`, y = `grade_terp_2 years degree`)) +
# geom_point() +
# geom_smooth(method= "lm") +
# theme_minimal() # removing bc only 2 datapoints for 3DMR @ timepoint 2
# Does Accuracy on the 3D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
mr3d_acc_grade_terp_wide %>%
ggplot(aes(x = `mr3d_acc_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title= "Does Accuracy on the 3D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Accuracy on the 3D Mental Rotation Task after 2 years") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
###### Correlations
mr3d_acc_grade_terp_wide %>%
filter(`mr3d_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_pre-degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 -0.127mr3d_acc_grade_terp_wide %>%
filter(`mr3d_acc_2 years degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>% summarize(AccCOR=cor(`mr3d_acc_2 years degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.901mr3d_acc_grade_terp_wide %>%
filter(`mr3d_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(AccCOR=cor(`mr3d_acc_pre-degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.221mr3d_acc_grade_terp_wide %>%
filter(`mr3d_acc_2 years degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>% summarize(AccCOR=cor(`mr3d_acc_2 years degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## AccCOR
## <dbl>
## 1 0.552RT
# Create wider pivot table
mr3d_rt_grade_terp_wide <- apt %>%
select(session, group, ppt, mr3d_rt, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(mr3d_rt, grade_terp))
# Does Reaction Time on the 3D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 1st year?
mr3d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr3d_rt_pre-degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Reaction Time on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
# Does Reaction Time on the 3D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 1st year?
# mr3d_rt_grade_terp_wide %>%
# ggplot(aes(x = `mr3d_rt_1 year degree`, y = `grade_terp_1 year degree`)) +
# geom_point() +
# geom_smooth(method= "lm") +
# theme_minimal() # removing bc only 2 datapoints for 3DMR @ timepoint 2
# Does Reaction Time on the 3D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 1st year?
mr3d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr3d_rt_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 1st year?", y = "1st year Interpreting grade", x = "Reaction Time on the 3D Mental Rotation Task after 2 year of study") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 30 rows containing non-finite values (stat_smooth).## Warning: Removed 30 rows containing missing values (geom_point).
# Does Reaction Time on the 3D Mental Rotation Task pre-degree predict Interpreting Grades at the end of 2nd year?
mr3d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr3d_rt_pre-degree`, y = `grade_terp_2 years degree`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal()+
labs(title= "Does Reaction Time on the 3D Mental Rotation Task pre-degree\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Reaction Time on the 3D Mental Rotation Task pre-degree") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 17 rows containing non-finite values (stat_smooth).## Warning: Removed 17 rows containing missing values (geom_point).
# Does Reaction Time on the 3D Mental Rotation Task after 1 year of study predict Interpreting Grades at the end of 2nd year?
# mr3d_rt_grade_terp_wide %>%
# ggplot(aes(x = `mr3d_rt_1 year degree`, y = `grade_terp_2 years degree`)) +
# geom_point() +
# geom_smooth(method= "lm") +
# theme_minimal() # removing bc only 2 datapoints for 3DMR @ timepoint 2
# Does Reaction Time on the 3D Mental Rotation Task after 2 years of study predict Interpreting Grades at the end of 2nd year?
mr3d_rt_grade_terp_wide %>%
ggplot(aes(x = `mr3d_rt_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Reaction Time on the 3D Mental Rotation Task after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = "Reaction Time on the 3D Mental Rotation Task after 2 years of study") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
Correlations
mr3d_rt_grade_terp_wide %>%
filter(`mr3d_rt_pre-degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_pre-degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 0.189mr3d_rt_grade_terp_wide %>%
filter(`mr3d_rt_2 years degree`!= "NA") %>%
filter(`grade_terp_1 year degree` != "NA") %>% summarize(rtCOR=cor(`mr3d_rt_2 years degree`,
`grade_terp_1 year degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.998mr3d_rt_grade_terp_wide %>%
filter(`mr3d_rt_pre-degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>%
summarize(rtCOR=cor(`mr3d_rt_pre-degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.487mr3d_rt_grade_terp_wide %>%
filter(`mr3d_rt_2 years degree`!= "NA") %>%
filter(`grade_terp_2 years degree` != "NA") %>% summarize(rtCOR=cor(`mr3d_rt_2 years degree`,
`grade_terp_2 years degree`))## # A tibble: 1 x 1
## rtCOR
## <dbl>
## 1 -0.184Barratt Impulsiveness Scale vs Interpreting Grades
Total Impulsiveness
# Create wider pivot table
bis_tot_grade_terp_wide <- apt %>%
select(session, group, ppt, bis_tot, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_tot, grade_terp))
# Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study predict Interpreting Grades at the end of 1st year?
bis_tot_grade_terp_wide %>%
filter(`bis_tot_2 years degree` != "na") %>%
filter(`grade_terp_1 year degree` != "na") %>%
ggplot(aes(x = `bis_tot_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'
# Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study predict Interpreting Grades at the end of 2nd year?
bis_tot_grade_terp_wide %>%
filter(`bis_tot_2 years degree` != "na") %>%
filter(`grade_terp_2 years degree` != "na") %>%
ggplot(aes(x = `bis_tot_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Total Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'
Not sure why geom_smooth() line does not appear in knitted output (works in .Rmd file)
Attentional Impulsiveness
# Create wider pivot table
bis_att_grade_terp_wide <- apt %>%
select(session, group, ppt, bis_att, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_att, grade_terp))
# Does Attentional Impulsiveness after 2 years predict Interpreting Grades at the end of 1st year?
bis_att_grade_terp_wide %>%
filter(`bis_att_2 years degree` != "na") %>%
ggplot(aes(x = `bis_att_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Attentional Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 10 rows containing non-finite values (stat_smooth).## Warning: Removed 10 rows containing missing values (geom_point).
# Does Attentional Impulsiveness after 2 years predict Interpreting Grades at the end of 2nd year?
bis_att_grade_terp_wide %>%
filter(`bis_att_2 years degree` != "na") %>%
ggplot(aes(x = `bis_att_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Attentional Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
Motor Impulsiveness
# Create wider pivot table
bis_nplan_grade_terp_wide <- apt %>%
select(session, group, ppt, bis_nplan, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_nplan, grade_terp))
# Does Motor Impulsiveness on the Barratt Impulsiveness Scale after 2 years predict Interpreting Grades at the end of 1st year?
bis_nplan_grade_terp_wide %>%
filter(`bis_nplan_2 years degree` != "na") %>%
ggplot(aes(x = `bis_nplan_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Motor Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 10 rows containing non-finite values (stat_smooth).## Warning: Removed 10 rows containing missing values (geom_point).
# Does Motor Impulsiveness on the Barratt Impulsiveness Scale after 2 years predict Interpreting Grades at the end of 2nd year?
bis_nplan_grade_terp_wide %>%
filter(`bis_nplan_2 years degree` != "na") %>%
ggplot(aes(x = `bis_nplan_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Motor Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
Non-Planning Impulsiveness
# Create wider pivot table
bis_mot_grade_terp_wide <- apt %>%
select(session, group, ppt, bis_mot, grade_terp) %>%
tidyr::pivot_wider(
names_from = session,
values_from = c(bis_mot, grade_terp))
# Does Non-PLanning Impulsiveness on the Barratt Impulsiveness Scale after 2 years predict Interpreting Grades at the end of 1st year?
bis_mot_grade_terp_wide %>%
filter(`bis_mot_2 years degree` != "NA") %>%
ggplot(aes(x = `bis_mot_2 years degree`, y = `grade_terp_1 year degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Non-Planning Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 10 rows containing non-finite values (stat_smooth).## Warning: Removed 10 rows containing missing values (geom_point).
# Does Non-PLanning Impulsiveness on the Barratt Impulsiveness Scale after 2 years predict Interpreting Grades at the end of 2nd year?
bis_mot_grade_terp_wide %>%
filter(`bis_mot_2 years degree` != "na") %>%
ggplot(aes(x = `bis_mot_2 years degree`, y = `grade_terp_2 years degree`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Does Non-Planning Impulsiveness on the Barratt Impulsiveness Scale after 2 years of study\npredict Interpreting Grades at the end of 2nd year?", y = "2nd year Interpreting grade", x = " ")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
######################################################################### #########################################################################
Factor Analysis
reduce tibble to just independent variables:
# apt <- select(apt, -session, - group, -ppt, -copy_score, -srt_score, -town_map, -bsl_grade)filter out rows with incomplete data
#apt_reduced <- filter(apt, corsi_corr != "na")
#apt_reduced <- filter(apt_reduced, nback_spat != "na")
#apt_reduced <- filter(apt_reduced, kirk_ceil != "na")reduce to just four tasks that were done at all 3 sessions
#apt_reduced <- select(apt_reduced, nback_lett:kbit_acc)FA using psych package
# fapsych <- fa(apt_reduced, nfactors=3, rotate="oblimin", scores="regression", residuals=FALSE, fm="minres")run FA using factanal package
# FA <- factanal(apt_reduced, factors=2, rotation="varimax", scores="regression")